Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review
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paper_title: Wireless capsule endoscopy

paper_content:

Gastroscopy, small bowel endoscopy, and colonoscopy are uncomfortable because they require comparatively large diameter flexible cables to be pushed into the bowel, which carry light by fibreoptic bundles, power, and video signals. Small bowel endoscopy is currently especially limited by problems of discomfort and failure to advance enteroscopes far into the small bowel. There is a clinical need for better methods to examine the small bowel especially in patients with recurrent gastrointestinal bleeding from this site.
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paper_title: Towards automatic polyp detection with a polyp appearance model

paper_content:

This work aims at automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside.
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paper_title: A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography

paper_content:

Adenomatous polyps in the colon are believed to be the precursor to colorectal carcinoma, the second leading cause of cancer deaths in United States. In this paper, we propose a new method for computer-aided detection of polyps in computed tomography (CT) colonography (virtual colonoscopy), a technique in which polyps are imaged along the wall of the air-inflated, cleansed colon with X-ray CT. Initial work with computer aided detection has shown high sensitivity, but at a cost of too many false positives. We present a statistical approach that uses support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue, and uses this information for the classification of the new cases. One of the main contributions of the paper is the new three-dimensional pattern processing approach, called random orthogonal shape sections method, which combines the information from many random images to generate reliable signatures of shape. The input to the proposed system is a collection of volume data from candidate polyps obtained by a high-sensitivity, low-specificity system that we developed previously. The results of our tenfold cross-validation experiments show that, on the average, the system increases the specificity from 0.19 (0.35) to 0.69 (0.74) at a sensitivity level of 1.0 (0.95).
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paper_title: Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours

paper_content:

Wireless capsule endoscopy (WCE) provides an inner view of the human digestive system. The inner tubular like structure of the intestinal tract consists of two major regions: lumen - intermediate region where the capsule moves, mucosa - membrane lining the lumen cavities. We study the use of the Split Bregman version of the extended active contour model of Chan and Vese for segmenting mucosal regions in WCE videos. Utilizing this segmentation we obtain a 3D reconstruction of the mucosal tissues using a near source perspective shape-from-shading (SfS) technique. Numerical results indicate that the active contour based segmentation provides better segmentations compared to previous methods and in turn gives better 3D reconstructions of mucosal regions.
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paper_title: Automatic polyp detection: A comparative study

paper_content:

In this work we present a performance comparison between a set of different state-of-the-art image descriptors for the automatic detection of polyps in colonoscopy videos. This set includes: Local binary patterns, 2-dimensional Gabor filters, wavelet-based texture, and histogram of oriented gradients. We use these descriptors in conjunction with support vector machine or nearest neighbor classifiers to classify candidate regions, which in turn are selected using the maximally stable extremal regions algorithm. We present performance scores on the ASU-Mayo Clinic polyp database.
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paper_title: Polyp Enhancing Level Set Evolution of Colon Wall: Method and Pilot Study

paper_content:

Computer aided detection (CAD) in computed tomography colonography (CTC) aims at detecting colonic polyps that are the precursors of colon cancer. In this work, we propose a colon wall evolution algorithm polyp enhancing level sets (PELS) based on the level-set formulation that regularizes and enhances polyps as a preprocessing step to CTC CAD algorithms. The underlying idea is to evolve the polyps towards spherical protrusions on the colon wall while keeping other structures, such as haustral folds, relatively unchanged and, thereby, potentially improve the performance of CTC CAD algorithms, especially for smaller polyps. To evaluate our methods, we conducted a pilot study using an arbitrarily chosen CTC CAD method, the surface normal overlap (SNO) CAD algorithm, on a nine patient CTC data set with 47 polyps of sizes ranging from 2.0 to 17.0 mm in diameter. PELS increased the maximum sensitivity by 8.1% (from 21/37 to 24/37) for small polyps of sizes ranging from 5.0 to 9.0 mm in diameter. This is accompanied by a statistically significant separation between small polyps and false positives. PELS did not change the CTC CAD performance significantly for larger polyps.
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paper_title: Polyp Detection in Colonoscopy Video using Elliptical Shape Feature

paper_content:

Early detection of polyps and cancers is one of the most important goals of colonoscopy. Computer-based analysis of video files using texture features, as has been proposed for polyps of the stomach and colon, has two major limitations: this method uses a fixed size analysis window and relies heavily on a training set of images for accuracy. To overcome these limitations, we propose a new technique focusing on shape instead of texture in this paper. The proposed polyp region detection method is based on the elliptical shape that is common for nearly all small colon polyps.
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paper_title: Color and Position versus Texture Features for Endoscopic Polyp Detection

paper_content:

This paper presents a comparison of texture based and color and position based methods for polyp detection in endoscopic video images. Two methods for texture feature extraction that presented good results in previous studies were implemented and their performance is compared against a simple combination of color and position features. Although this more simple approach produces a much higher number of features than the other approaches, a SVM with a KBF kernel is able to deal with this high dimensional input space and it turns out that it outperforms the previous approaches on the experiments performed in a database of 4620 images from endoscopic video.
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paper_title: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps

paper_content:

We have developed a three-dimensional (3-D) computer-aided diagnosis scheme for automated detection of colonic polyps in computed tomography (CT) colonographic data sets, and assessed its performance based on colonoscopy as the gold standard. In this scheme, a thick region encompassing the entire colonic wall is extracted from an isotropic volume reconstructed from the CT images in CT colonography. Polyp candidates are detected by first computing of 3-D geometric features that characterize polyps, folds, and colonic walls at each voxel in the extracted colon, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding based on these geometric features. We apply fuzzy clustering to these connected components to obtain the polyp candidates. False-positive (FP) detections are then reduced by computation of several 3-D volumetric features characterizing the internal structures of the polyp candidates, followed by the application of discriminant analysis to the feature space generated by these volumetric features. The locations of the polyps detected by our computerized method were compared to the gold standard of conventional colonoscopy. The performance was evaluated based on 43 clinical cases, including 12 polyps determined by colonoscopy. Our computerized scheme was shown to have the potential to detect polyps in CT colonography with a clinically acceptable high sensitivity and a low FP rate.
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paper_title: Capsule Endoscopy versus Colonoscopy for the Detection of Polyps and Cancer

paper_content:

Background An ingestible capsule consisting of an endoscope equipped with a video camera at both ends was designed to explore the colon. This study compared capsule endoscopy with optical colonoscopy for the detection of colorectal polyps and cancer. Methods We performed a prospective, multicenter study comparing capsule endoscopy with optical colonoscopy (the standard for comparison) in a cohort of patients with known or suspected colonic disease for the detection of colorectal polyps or cancer. Patients underwent an adapted colon preparation, and colon cleanliness was graded from poor to excellent. We computed the sensitivity and specificity of capsule endoscopy for polyps, advanced adenoma, and cancer. Results A total of 328 patients (mean age, 58.6 years) were included in the study. The capsule was excreted within 10 hours after ingestion and before the end of the lifetime of the battery in 92.8% of the patients. The sensitivity and specificity of capsule endoscopy for detecting polyps that were 6 mm ...
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paper_title: Automatic Contrast Enhancement for Wireless Capsule Endoscopy Videos with Spectral Optimal Contrast-Tone Mapping

paper_content:

Wireless capsule endoscopy (WCE) is a revolutionary imaging method for visualizing gastrointestinal tract in patients. Each exam of a patient creates large-scale color video data typically in hours and automatic computer aided diagnosis (CAD) are of important in alleviating the strain on expert gastroenterologists. In this work we consider an automatic contrast enhancement method for WCE videos by using an extension of the recently proposed optimal contrast-tone mapping (OCTM) to color images. By utilizing the transformation of each RGB color from of the endoscopy video to the spectral color space La*b* and utilizing the OCTM on the intensity channel alone we obtain our spectral OCTM (SOCTM) approach. Experimental results comparing histogram equalization, anisotropic diffusion and original OCTM show that our enhancement works well without creating saturation artifacts in real WCE imagery.
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paper_title: Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow

paper_content:

Today's computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps. We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm. We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.
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paper_title: Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT

paper_content:

We developed a novel computer-aided detection (CAD) algorithm called the surface normal overlap method that we applied to colonic polyp detection and lung nodule detection in helical computed tomography (CT) images. We demonstrate some of the theoretical aspects of this algorithm using a statistical shape model. The algorithm was then optimized on simulated CT data and evaluated using a per-lesion cross-validation on 8 CT colonography datasets and on 8 chest CT datasets. It is able to achieve 100% sensitivity for colonic polyps 10 mm and larger at 7.0 false positives (FPs)/dataset and 90% sensitivity for solid lung nodules 6 mm and larger at 5.6 FP/dataset.
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paper_title: Polyp Detection in Endoscopic Video Using SVMs

paper_content:

Colon cancer is one of the most common cancers in developed countries. Most of these cancers start with a polyp. Polyps are easily detected by physicians. Our goal is to mimic this detection ability so that endoscopic videos can be pre-scanned with our algorithm before the physician analyses them. The method will indicate which part of the video needs attention (polyps were detected there) and hence can speedup the procedures. In this paper we present a method for polyp detection in endoscopic images that uses SVM for classification. Our experiments yielded a result of 93.16 ± 0.09% of area under the Receiver Operating Characteristic (ROC) curve on a database of 4620 images indicating that the approach proposed is well suited to the detection of polyps in endoscopic video.
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paper_title: Colorectal Polyps Detection Using Texture Features and Support Vector Machine

paper_content:

In this paper we propose a novel method in detecting colorectal polyps on colonoscopic images. Texture features are applied in polyps and normal tissues training and classification. Support vector machine is used as a classifier to identify the position of polyps. Seventy-four colonoscopic images are collected to test the system. Half of them are used as training images and half are used as testing. The experimental result shows the system can identify all polyps if the colonoscopic images contain single polyp. The sensitivity is 86.2% and the false-positive rate is 1.26 mark per-image.
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paper_title: A Colon Video Analysis Framework for Polyp Detection

paper_content:

This paper presents an automated video analysis framework for the detection of colonic polyps in optical colonoscopy. Our proposed framework departs from previous methods in that we include spatial frame-based analysis and temporal video analysis using time-course image sequences. We also provide a video quality assessment scheme including two measures of frame quality. We extract colon-specific anatomical features from different image regions using a windowing approach for intraframe spatial analysis. Anatomical features are described using an eigentissue model. We apply a conditional random field to model interframe dependences in tissue types and handle variations in imaging conditions and modalities. We validate our method by comparing our polyp detection results to colonoscopy reports from physicians. Our method displays promising preliminary results and shows strong invariance when applied to both white light and narrow-band video. Our proposed video analysis system can provide objective diagnostic support to physicians by locating polyps during colon cancer screening exams. Furthermore, our system can be used as a cost-effective video annotation solution for the large backlog of existing colonoscopy videos.
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paper_title: Computer-Aided Decision Support Systems for Endoscopy in the Gastrointestinal Tract: A Review

paper_content:

Today, medical endoscopy is a widely used procedure to inspect the inner cavities of the human body. The advent of endoscopic imaging techniques-allowing the acquisition of images or videos-created the possibility for the development of the whole new branch of computer-aided decision support systems. Such systems aim at helping physicians to identify possibly malignant abnormalities more accurately. At the beginning of this paper, we give a brief introduction to the history of endoscopy, followed by introducing the main types of endoscopes which emerged so far (flexible endoscope, wireless capsule endoscope, and confocal laser endomicroscope). We then give a brief introduction to computer-aided decision support systems specifically targeted at endoscopy in the gastrointestinal tract. Then we present general facts and figures concerning computer-aided decision support systems and summarize work specifically targeted at computer-aided decision support in the gastrointestinal tract. This summary is followed by a discussion of some common issues concerning the approaches reviewed and suggestions of possible ways to resolve them.
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paper_title: Wireless capsule endoscopy

paper_content:

Gastroscopy, small bowel endoscopy, and colonoscopy are uncomfortable because they require comparatively large diameter flexible cables to be pushed into the bowel, which carry light by fibreoptic bundles, power, and video signals. Small bowel endoscopy is currently especially limited by problems of discomfort and failure to advance enteroscopes far into the small bowel. There is a clinical need for better methods to examine the small bowel especially in patients with recurrent gastrointestinal bleeding from this site.
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paper_title: Shape estimation of gastrointestinal polyps using motion information

paper_content:

Polyp size quanti cation is currently the main variable for deciding the patient treatment during an endoscopic procedure. Nowadays, the polyp size is estimated by an expert, even when using devices that are provided with calibrated grids. As such estimation is highly subjective, automatic approaches have come to be appealing but also challenging because the polyp shape and appearance variability, the di erent types of motion present during the capture and the specular highlight noise. This work presents a novel approach to automatically estimate gastrointestinal polyp shapes in a video endoscopic sequence using spatiotemporal information. For doing so, a local spatio temporal descriptor is built up to obtain an initial segmentation since the polyp is the region with more movement. Then, an initial polyp manual segmentation outlines a region of interest (RoI) in the rst frame of the sequence and used as a reference for the polyp tracking during the sequence. Afterward, an exhaustive cross-correlation of the initial shape is carried out along the sequence and fused with the motion descriptor to re ne the original segmentation. The proposed approach was evaluated in 15 real video sequences achieving an average DSC score of 0:67% .
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paper_title: Accuracy and safety of second-generation PillCam COLON capsule for colorectal polyp detection

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PillCam COLON capsule endoscopy (CCE) (Given Imaging Ltd, Yoqneam, Israel) is one of the most recent diagnostic, endoscopic technologies designed to explore the colon. CCE is a noninvasive, patient-friendly technique that is able to explore the colon without requiring sedation and air insufflation. The first generation of CCE was released onto the market in 2006 and although it generated great enthusiasm, it showed suboptimal accuracy. Recently, a second-generation system (PillCam COLON 2) (CCE-2) has been developed to increase sensitivity for colorectal polyp detection. In this review, accuracy and safety data for CCE-2 are analyzed.
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paper_title: Meta analysis shows Colon Capsule Endoscopy is effective in detecting Colorectal polyps.

paper_content:

Background & Aims Colon capsule endoscopy (CCE) is a noninvasive and painless technique used to explore the colon without sedation or air insufflation. We performed a systematic review and meta-analysis to assess the accuracy of CCE in detecting colorectal polyps. Methods The MEDLINE, EMBASE, and SCOPUS databases were searched, from 2006 to 2009, for the terms "colon capsule" and "Pillcam colon"; searches included abstracts. Studies were included that focused on detecting colorectal polyps with CCE and that were verified using within-subject reference colonoscopy. The risk of bias within each study was ascertained according to Quality Assessment of Diagnostic Accuracy in Systematic Reviews recommendations. The per-patient sensitivity and specificity were calculated for polyps of any size and for significant findings (polyps, ≥6 mm in size or >3 in number). Forest plots were produced based on random-effect models. The risk of bias across studies was assessed using the interstudy heterogeneity statistic, meta-regression, and the Egger test. Results Eight studies provided data on 837 patients; the prevalences of polyps and significant findings were 57% and 27.4%, respectively. CCE sensitivity for polyps of any size and significant findings were 71% and 68%, respectively. CCE specificity for polyps of any size and significant findings were 75% and 82%, respectively. High levels of heterogeneity (interstudy heterogeneity, >75%) were not detected. Moderate heterogeneity partially was explained by the different design of individual studies. CCE identified 16 of the 21 cancerous lesions detected by colonoscopy (pooled sensitivity, 76%). Conclusions CCE sensitivity for polyps and significant findings compares favorably with other noninvasive colorectal cancer screening strategies. CCE specificity is likely to be underestimated because reference colonoscopy examination results are blinded.
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paper_title: A Novel Segmentation and Navigation Method for Polyps Detection using Mathematical Morphology and Active Contour Models

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This paper describes the development of an automatic segmentation method and a novel navigation system that detect polyps using advanced image processing techniques. The segmentation method described here outlines the colon wall of the CT data set of abdomen by combining the active contouring model - level set method and the minima detection using mathematical morphology theory. The polyp detection is achieved by analysing the surface curvature and the texture information surrounded on the colon wall. As such, the polyp candidates are examined not only by their shape, size but also by their texture appearance which improves the current existing methods. The major contribution of our method is the detailed information analysis using the active contour model assisted by mathematical morphology operations and texture analysis.
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paper_title: Second-generation colon capsule endoscopy compared with colonoscopy

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Background Colon capsule endoscopy (CCE) represents a noninvasive technology that allows visualization of the colon without requiring sedation and air insufflation. A second-generation colon capsule endoscopy system (PillCam Colon 2) (CCE-2) was developed to increase sensitivity for colorectal polyp detection compared with the first-generation system. Objective To assess the feasibility, accuracy, and safety of CCE-2 in a head-to-head comparison with colonoscopy. Design and Setting Prospective, multicenter trial including 8 European sites. Patients This study involved 117 patients (mean age 60 years). Data from 109 patients were analyzed. Intervention CCE-2 was prospectively compared with conventional colonoscopy as the criterion standard for the detection of colorectal polyps that are ≥6 mm or masses in a cohort of patients at average or increased risk of colorectal neoplasia. Colonoscopy was independently performed within 10 hours after capsule ingestion or on the next day. Main Outcome Measurements CCE-2 sensitivity and specificity for detecting patients with polyps ≥6 mm and ≥10 mm were assessed. Capsule-positive but colonoscopy-negative cases were counted as false positive. Capsule excretion rate, level of bowel preparation, and rate of adverse events also were assessed. Results Per-patient CCE-2 sensitivity for polyps ≥6 mm and ≥10 mm was 84% and 88%, with specificities of 64% and 95%, respectively. All 3 invasive carcinomas were detected by CCE-2. The capsule excretion rate was 88% within 10 hours. Overall colon cleanliness for CCE-2 was adequate in 81% of patients. Limitations Not unblinding the CCE-2 results at colonoscopy; heterogenous patient population; nonconsecutive patients. Conclusion In this European, multicenter study, CCE-2 appeared to have a high sensitivity for the detection of clinically relevant polypoid lesions, and it might be considered an adequate tool for colorectal imaging.
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paper_title: Automatic colon polyp detection in endoscopic capsule images

paper_content:

This paper presents a method of automatic detection of colon polyps in endoscopic capsule images. This approach uses color and geometrical analysis of the images and uses histogram of gradients features and multilayer perceptron neural networks for performing the detection at multiple scales. The detection performance is tested using a database of 30540 images, 540 of them containing at least one polyp.
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paper_title: Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours

paper_content:

Wireless capsule endoscopy (WCE) provides an inner view of the human digestive system. The inner tubular like structure of the intestinal tract consists of two major regions: lumen - intermediate region where the capsule moves, mucosa - membrane lining the lumen cavities. We study the use of the Split Bregman version of the extended active contour model of Chan and Vese for segmenting mucosal regions in WCE videos. Utilizing this segmentation we obtain a 3D reconstruction of the mucosal tissues using a near source perspective shape-from-shading (SfS) technique. Numerical results indicate that the active contour based segmentation provides better segmentations compared to previous methods and in turn gives better 3D reconstructions of mucosal regions.
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paper_title: Identification of polyps in Wireless Capsule Endoscopy videos using Log Gabor filters

paper_content:

Wireless Capsule Endoscopy (WCE) is a very useful technology that enables gastroenterologists to examine the human digestive tract and more particularly, the small bowel, searching for various abnormalities like blood-based abnormalities, ulcers and polyps. Each WCE video consists of approximately 50,000 frames making its examination a very tedious task. In this paper a methodology is proposed for the automatic detection of polyps. It utilizes the very promising Log Gabor filters as a segmentation scheme, in conjunction with the powerful SUSAN edge detector. After segmentation process, geometric information of the resulted segments is extracted to identify polyp candidates. Furthermore, certain rules apply to limit the number of false positive detections. Illustrative and statistical results of the methodology are also given in this paper.
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paper_title: Polyp detection in Wireless Capsule Endoscopy videos based on image segmentation and geometric feature

paper_content:

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. One of the most important goals of WCE is the early detection of colorectal polyps. In this paper an unsupervised method for the detection of polyps in WCE videos is presented. Our method involves watershed segmentation with a novel initial marker selection method based on Gabor texture features and K-means clustering. Geometric information from the resulting segments is extracted to identify polyp candidates. Initial experiments indicate that the proposed method can detect polyps with 100% sensitivity and over 81% specificity.
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paper_title: Automatic detection of colonoscopic anomalies using capsule endoscopy

paper_content:

Colon cancer and precancerous colon lesions are major health problems. The most frequent precancerous cancer lesions are polyps characterized by abnormal tissue growth in the human bowel. There are also anomalies such as inflammation and bleeding area. Colon capsule endoscopy (CCE) is a recent promising technology which enables to obtain videos of the inside of the intestine via an on board digital camera. The small capsule ingested by the patient is recuperated after it passes through the whole gastrointestinal track. Expert gastroenterologists analyze the video sequence visually in order to find out frames containing abnormalities related to cancer. This process is labor intensive and time consuming. In this paper we propose an algorithm that provides automatic video analysis in order to classify tissue regions in images into two categories: normal and abnormal. In order to achieve high correct classification rates, we first pre-process the image data by removing the noise in it and by normalizing the intensity values of the image pixels. Next, we use the well-known SIFT descriptor algorithm with Bag of Feature (BoF) approach for feature extraction. For training, Support Vector Machine (SVM) is trained on the extracted features using the training dataset. Finally a testing data set is used to assess the performance of the proposed algorithm. The early experimental results are very encouraging and show high correct classification rates, reaching up to 98.25% for images with polyps. The novelty of the proposed algorithm is the combination of using specific SIFT features with Bof and the vignetting correction to capture the local characteristics of the abnormalities in capsule video endoscopy images.
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paper_title: Polyp detection in wireless capsule endoscopy images using novel color texture features

paper_content:

Wireless capsule endoscopy (WCE) has been widely used nowadays to view the entire intestine. However, there still exists a challenging problem of it that over 55,000 images are produced during one examination for one patient. Analyzing such large number of images is very time-consuming and tedious for physicians, so we propose a computer aided diagnosis (CAD) strategy to automatically detect the polyps. Hopefully it will reduce this heavy burden for doctors. In this paper, we propose a novel opponent color feature integrating with LLE-based LBP texture feature. These two types of features are used to distinguish the polyp images from the normal ones using support vector machine (SVM). Experiments are performed on our current data set to verify the proposed scheme. The classification accuracy reaches 97% which shows that the proposed method is very efficient and promising.
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paper_title: Abnormal Image Detection Using Texton Method in Wireless Capsule Endoscopy Videos

paper_content:

One of the main goals of Wireless Capsule Endoscopy (WCE) is to detect the mucosal abnormalities such as blood, ulcer, polyp, and so on in the gastrointestinal tract. Only less than 5% of total 55,000 frames of a WCE video typically have abnormalities, so it is critical to develop a technique to automatically discriminate abnormal findings from normal ones. We introduce “Texton” method which has been successfully used for image texture classification in non-medical domains. A histogram of Textons (exemplar responses occurring after convolving an image with a set of filters called “Filter bank”) called a “Texton Histogram” is used to represent an abnormal or a normal region. Then, a classifier (i.e., SVM or K-NN, and etc.) is trained using the Texton Histograms to distinguish images with abnormal regions from ones without them. Experimental results on our current data set show that the proposed method achieves promising performances.
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paper_title: SUSAN—A New Approach to Low Level Image Processing

paper_content:

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. ::: ::: Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. ::: ::: Details of the new feature detectors and of the new noise reduction method are described, along with test results.
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paper_title: Automated polyp detection in colon capsule endoscopy

paper_content:

Colorectal polyps are important precursors to colon cancer, a major health problem. Colon capsule endoscopy is a safe and minimally invasive examination procedure, in which the images of the intestine are obtained via digital cameras on board of a small capsule ingested by a patient. The video sequence is then analyzed for the presence of polyps. We propose an algorithm that relieves the labor of a human operator analyzing the frames in the video sequence. The algorithm acts as a binary classifier, which labels the frame as either containing polyps or not, based on the geometrical analysis and the texture content of the frame.We assume that the polyps are characterized as protrusions that are mostly round in shape. Thus, a best fit ball radius is used as a decision parameter of the classifier. We present a statistical performance evaluation of our approach on a data set containing over 18 900 frames from the endoscopic video sequences of five adult patients. The algorithm achieves 47% sensitivity per frame and 81% sensitivity per polyp at a specificity level of 90%. On average, with a video sequence length of 3747 frames, only 367 false positive frames need to be inspected by an operator.
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paper_title: Automatic Polyp Detection in Pillcam Colon 2 Capsule Images and Videos: Preliminary Feasibility Report

paper_content:

Background. The aim of this work is to present an automatic colorectal polyp detection scheme for capsule endoscopy. Methods. PillCam COLON2 capsule-based images and videos were used in our study. The database consists of full exam videos from five patients. The algorithm is based on the assumption that the polyps show up as a protrusion in the captured images and is expressed by means of a P-value, defined by geometrical features. Results. Seventeen PillCam COLON2 capsule videos are included, containing frames with polyps, flat lesions, diverticula, bubbles, and trash liquids. Polyps larger than 1 cm express a P-value higher than 2000, and 80% of the polyps show a P-value higher than 500. Diverticula, bubbles, trash liquids, and flat lesions were correctly interpreted by the algorithm as nonprotruding images. Conclusions. These preliminary results suggest that the proposed geometry-based polyp detection scheme works well, not only by allowing the detection of polyps but also by differentiating them from nonprotruding images found in the films.
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paper_title: Segmentation and detection of colorectal polyps using local polynomial approximation

paper_content:

In this paper we introduce a new methodology to segment and detect colorectal polyps in endoscopic images obtained by a wireless capsule endoscopic device. The cornerstone of our approach is the fact that polyps are protrusions emerging from colonic walls. Thus, they can be segmented by simple curvature descriptors. Curvature is based on derivatives, thus very sensitive to noise and image artifacts. Furthermore, the acquired images are sampled on a grid which further complicates the computation of derivatives. To cope with these degradation mechanisms, we use use Local Polynomial Approximation, which, simultaneously, denoise the observed images and provides a continuous representation suitable to compute derivatives. On the top of the image segmentation, we built a support vector machine to classify the segmented regions as polyps or non-polyps. The features used in the classifier are selected with a wrapper selection algorithm (greedy forward feature selection algorithm with support vector machines). The proposed segmentation and detection methodology is tested in several scenarios presenting very good results both using the same video sequences as training data and testing data (cross-feature validation) and different video sequences as training and testing data.
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paper_title: Polyp detection and radius measurement in small intestine using video capsule endoscopy

paper_content:

Video Capsule Endoscopy (VCE) was invented in the year 2000 and rapidly became one of the most popular noninvasive non-surgical inspection techniques in the diagnosis of gastrointestinal (GI) tract, especially in small intestine. A critical issue of capsule endoscopic examination is to determine the location and size of polyps. A critical problem associated with capsule endoscopic examination is to localize and correctly estimate the size of polyps for proper clinical treatment. In this paper we present a global statistical method which could automatically detect the polyps in VCE frames and determine their radii. The proposed method gathers the statistical information from available RGB channels. The statistical information is then fed to a support vector machine (SVM) to determine the existence and radii of polyps. The experimental result of this approach shows its improvement of accuracy when compared with other methods in the literature.
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paper_title: Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos

paper_content:

Over the last decade, wireless capsule endoscopy (WCE) technology has become a very useful tool for diagnosing diseases within the human digestive tract. Physicians using WCE can examine the digestive tract in a minimally invasive way searching for pathological abnormalities such as bleeding, polyps, ulcers, and Crohn's disease. To improve effectiveness of WCE, researchers have developed software methods to automatically detect these diseases at a high rate of success. This paper proposes a novel synergistic methodology for automatically discovering polyps (protrusions) and perforated ulcers in WCE video frames. Finally, results of the methodology are given and statistical comparisons are also presented relevant to other works.
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paper_title: Vascularization features for polyp localization in capsule endoscopy

paper_content:

Polyps in gastrointestinal (GI) tract can be precursors to cancer and detecting them early is important in determining their malignancy. Video capsule endoscopy imaging is a useful technology and provides visualization of GI without discomfort to the patient. We consider polyp localization from capsule endoscopy imagery using vascularization features. By using texture features computed from principle curvatures of the image surface, and multiscale directional vesselness stamping we obtain localization of polyps from a given video frame. We present our preliminary results for malignant and benign polyps from capsule endoscopy images.
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paper_title: Identification of polyps in Wireless Capsule Endoscopy videos using Log Gabor filters

paper_content:

Wireless Capsule Endoscopy (WCE) is a very useful technology that enables gastroenterologists to examine the human digestive tract and more particularly, the small bowel, searching for various abnormalities like blood-based abnormalities, ulcers and polyps. Each WCE video consists of approximately 50,000 frames making its examination a very tedious task. In this paper a methodology is proposed for the automatic detection of polyps. It utilizes the very promising Log Gabor filters as a segmentation scheme, in conjunction with the powerful SUSAN edge detector. After segmentation process, geometric information of the resulted segments is extracted to identify polyp candidates. Furthermore, certain rules apply to limit the number of false positive detections. Illustrative and statistical results of the methodology are also given in this paper.
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paper_title: Vascularization features for polyp localization in capsule endoscopy

paper_content:

Polyps in gastrointestinal (GI) tract can be precursors to cancer and detecting them early is important in determining their malignancy. Video capsule endoscopy imaging is a useful technology and provides visualization of GI without discomfort to the patient. We consider polyp localization from capsule endoscopy imagery using vascularization features. By using texture features computed from principle curvatures of the image surface, and multiscale directional vesselness stamping we obtain localization of polyps from a given video frame. We present our preliminary results for malignant and benign polyps from capsule endoscopy images.
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paper_title: Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours

paper_content:

Wireless capsule endoscopy (WCE) provides an inner view of the human digestive system. The inner tubular like structure of the intestinal tract consists of two major regions: lumen - intermediate region where the capsule moves, mucosa - membrane lining the lumen cavities. We study the use of the Split Bregman version of the extended active contour model of Chan and Vese for segmenting mucosal regions in WCE videos. Utilizing this segmentation we obtain a 3D reconstruction of the mucosal tissues using a near source perspective shape-from-shading (SfS) technique. Numerical results indicate that the active contour based segmentation provides better segmentations compared to previous methods and in turn gives better 3D reconstructions of mucosal regions.
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paper_title: Identification of polyps in Wireless Capsule Endoscopy videos using Log Gabor filters

paper_content:

Wireless Capsule Endoscopy (WCE) is a very useful technology that enables gastroenterologists to examine the human digestive tract and more particularly, the small bowel, searching for various abnormalities like blood-based abnormalities, ulcers and polyps. Each WCE video consists of approximately 50,000 frames making its examination a very tedious task. In this paper a methodology is proposed for the automatic detection of polyps. It utilizes the very promising Log Gabor filters as a segmentation scheme, in conjunction with the powerful SUSAN edge detector. After segmentation process, geometric information of the resulted segments is extracted to identify polyp candidates. Furthermore, certain rules apply to limit the number of false positive detections. Illustrative and statistical results of the methodology are also given in this paper.
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paper_title: Polyp detection in Wireless Capsule Endoscopy videos based on image segmentation and geometric feature

paper_content:

Wireless Capsule Endoscopy (WCE) is a relatively new technology (FDA approved in 2002) allowing doctors to view most of the small intestine. One of the most important goals of WCE is the early detection of colorectal polyps. In this paper an unsupervised method for the detection of polyps in WCE videos is presented. Our method involves watershed segmentation with a novel initial marker selection method based on Gabor texture features and K-means clustering. Geometric information from the resulting segments is extracted to identify polyp candidates. Initial experiments indicate that the proposed method can detect polyps with 100% sensitivity and over 81% specificity.
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paper_title: Computer-Assisted Segmentation of Videocapsule Images Using Alpha-Divergence-Based Active Contour in the Framework of Intestinal Pathologies Detection

paper_content:

Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the "gold standard" technique for diagnosis of different pathologies of the intestinal tract. Wireless capsule endoscopy (WCE) has been developed in the 1990s as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic postexamination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using WCE remains time-consuming and challenging due to the poor quality of WCE images. In this paper, a semiautomatic segmentation for analysis of WCE images is proposed. Based on active contour segmentation, the proposed method introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies. Results of segmentation using the proposed approach are shown on different types of real-case examinations, from (multi)polyp(s) segmentation, to radiation enteritis delineation.
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paper_title: Automatic Polyp Detection in Pillcam Colon 2 Capsule Images and Videos: Preliminary Feasibility Report

paper_content:

Background. The aim of this work is to present an automatic colorectal polyp detection scheme for capsule endoscopy. Methods. PillCam COLON2 capsule-based images and videos were used in our study. The database consists of full exam videos from five patients. The algorithm is based on the assumption that the polyps show up as a protrusion in the captured images and is expressed by means of a P-value, defined by geometrical features. Results. Seventeen PillCam COLON2 capsule videos are included, containing frames with polyps, flat lesions, diverticula, bubbles, and trash liquids. Polyps larger than 1 cm express a P-value higher than 2000, and 80% of the polyps show a P-value higher than 500. Diverticula, bubbles, trash liquids, and flat lesions were correctly interpreted by the algorithm as nonprotruding images. Conclusions. These preliminary results suggest that the proposed geometry-based polyp detection scheme works well, not only by allowing the detection of polyps but also by differentiating them from nonprotruding images found in the films.
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paper_title: Polyps auto-detection in Wireless Capsule Endoscopy images using improved method based on image segmentation

paper_content:

Wireless Capsule Endoscopy (WCE) is a noninvasive instrument that widely used in screening the whole intestine and it has been utilized as a model especially for the examination of gastrointestinal (GI) diseases. However, it is numerous images of the detecting result produced by WCE that always burdens the physicians. To solve this problem, it is necessary to combine the manual diagnosis with the image segmentation technology. In this paper we proposed a feasible method by using K-means clustering and localizing region-based active contour segmentation for polyps auto-detection in WCE images. Experimental results shows the method is promising and efficient.
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paper_title: Segmentation of Small Bowel Tumors in Wireless Capsule Endoscopy Using Level Set Method

paper_content:

In this paper, we proposed an algorithm to segment small bowel tumors. In order to increase effectiveness of Level Set Method (LSM) we applied adaptive gamma correction method (AGCM) that is based on prior information of illumination of images. We applied this method on 10 small bowel tumor images captured by Wireless Capsule Endoscopy (WCE). The performance measurements (i.e. sensitivity, specificity, and accuracy) by using hand ground method are computed for different parameters of a (0.05, 0.07, 0.09, 0.11, and 0.13) in AGCM, and then compared with traditional LSM and Snake method. The proposed method shows increased sensitivity up to 0.87 in a=0.13 while other performance measurements decrease by increasing value of a. the sensitivity of the other methods are 0.2 and 0.22, respectively. The optimal value of these measurements is 0.73 that takes place in a=0.1.
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paper_title: Polyp detection in Wireless Capsule Endoscopy images by using region-based active contour model

paper_content:

Wireless Capsule Endoscopy (WCE) is an almost new technology to capture images from the whole of Gastrointestinal (GI) tract, noninvasively. WCE is a very useful technology to detect various abnormalities like blood based abnormalities, ulcers and polyps. We note that polyps are growing tissues occur on the surface of tissue instead of inside an organ. Most polyps are not cancerous but if one becomes larger than a centimeter, it can turn into cancer by great chance. So, one of the most important advantages of WCE can be the early detection of polyps and cancers. In this paper we proposed using region-based Active Contour Method (ACM) and geometric feature for automatic detection of polyps. The results on a set of images show that the proposed method can achieve %90.91 accuracy, %100 sensitivity and %83.33 specificity on our data set.
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paper_title: Towards automatic polyp detection with a polyp appearance model

paper_content:

This work aims at automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside.
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paper_title: Mucosal region detection and 3D reconstruction in wireless capsule endoscopy videos using active contours

paper_content:

Wireless capsule endoscopy (WCE) provides an inner view of the human digestive system. The inner tubular like structure of the intestinal tract consists of two major regions: lumen - intermediate region where the capsule moves, mucosa - membrane lining the lumen cavities. We study the use of the Split Bregman version of the extended active contour model of Chan and Vese for segmenting mucosal regions in WCE videos. Utilizing this segmentation we obtain a 3D reconstruction of the mucosal tissues using a near source perspective shape-from-shading (SfS) technique. Numerical results indicate that the active contour based segmentation provides better segmentations compared to previous methods and in turn gives better 3D reconstructions of mucosal regions.
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paper_title: Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding

paper_content:

Video capsule endoscopy (VCE) is a revolutionary imaging technique widely used in visualizing the gastrointestinal tract. The amount of big data generated by VCE necessitates automatic computed aided-diagnosis (CAD) systems to aid the experts in making clinically relevant decisions. In this work, we consider an automatic tissue detection method that uses an adaptive entropy thresholding for better separation of mucosa which lines the colon wall from lumen, which is the hollowed gastrointestinal tract. Comparison with other thresholding methods such as Niblack, Bernsen, Otsu, and Sauvola as well as active contour is undertaken. Experimental results indicate that our method performs better than other methods in terms of segmentation accuracy in various VCE videos.
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paper_title: Computer-Assisted Segmentation of Videocapsule Images Using Alpha-Divergence-Based Active Contour in the Framework of Intestinal Pathologies Detection

paper_content:

Visualization of the entire length of the gastrointestinal tract through natural orifices is a challenge for endoscopists. Videoendoscopy is currently the "gold standard" technique for diagnosis of different pathologies of the intestinal tract. Wireless capsule endoscopy (WCE) has been developed in the 1990s as an alternative to videoendoscopy to allow direct examination of the gastrointestinal tract without any need for sedation. Nevertheless, the systematic postexamination by the specialist of the 50,000 (for the small bowel) to 150,000 images (for the colon) of a complete acquisition using WCE remains time-consuming and challenging due to the poor quality of WCE images. In this paper, a semiautomatic segmentation for analysis of WCE images is proposed. Based on active contour segmentation, the proposed method introduces alpha-divergences, a flexible statistical similarity measure that gives a real flexibility to different types of gastrointestinal pathologies. Results of segmentation using the proposed approach are shown on different types of real-case examinations, from (multi)polyp(s) segmentation, to radiation enteritis delineation.
---
paper_title: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

paper_content:

Purpose Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested.Methods First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method’s performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself.Results The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images.Conclusion A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.
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paper_title: Segmentation of video capsule endoscopic images using alpha-divergence based active contour method

paper_content:

Examination of the whole gastrointestinal tract represents a challenge for endoscopists due to its length and inaccessibility using natural orifices. Moreover, radiologic techniques are relatively insensitive for diminutive, flat, infiltrative, or inflammatory lesions of the small bowel. Since 1994, video capsules (VCEs) have been developed to allow direct examination of this inaccessible part of the gastrointestinal tract and to help doctors to find the cause of symptoms such as stomach pain, disease of Crohn, diarrhoea, weight loss, rectal bleeding, and anaemia. The Pillcam© video capsule designed by Given Imaging Company is the most popular of them. This autonomous embedded system allows acquiring about fifty thousand images of gastrointestinal tract during more than twelve hours of an analysis. However, acquired images are of low resolution and imaging conditions could be very poor as the movements and the speed of the VCE as illumination are not entirely controlled all along the transit of the capsule through the whole gastrointestinal tract. As a consequence segmentation of VCE images is a real challenging problem: first of all specialists need a flexible segmentation tool that makes delineations of very different structures (polyps, metastasis... to name a few) possible and second, the proposed method should be efficient even in difficult imaging conditions. In this article, an adapted segmentation method based on statistical active contours for VCE segmentation is proposed and tested on various types of gastrointestinal structures.
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paper_title: Segmentation of Small Bowel Tumors in Wireless Capsule Endoscopy Using Level Set Method

paper_content:

In this paper, we proposed an algorithm to segment small bowel tumors. In order to increase effectiveness of Level Set Method (LSM) we applied adaptive gamma correction method (AGCM) that is based on prior information of illumination of images. We applied this method on 10 small bowel tumor images captured by Wireless Capsule Endoscopy (WCE). The performance measurements (i.e. sensitivity, specificity, and accuracy) by using hand ground method are computed for different parameters of a (0.05, 0.07, 0.09, 0.11, and 0.13) in AGCM, and then compared with traditional LSM and Snake method. The proposed method shows increased sensitivity up to 0.87 in a=0.13 while other performance measurements decrease by increasing value of a. the sensitivity of the other methods are 0.2 and 0.22, respectively. The optimal value of these measurements is 0.73 that takes place in a=0.1.
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paper_title: Automatic Image Segmentation for Video Capsule Endoscopy

paper_content:

Video capsule endoscopy (VCE) has proven to be a pain-free imaging technique of gastrointestinal (GI) tract and provides continuous stream of color imagery. Due to the amount of images captured automatic computer-aided diagnostic (CAD) methods are required to reduce the burden of gastroenterologists. In this work, we propose a fast and efficient method for obtaining segmentations of VCE images automatically without manual supervision. We utilize an efficient active contour without edges model which accounts for topological changes of the mucosal surface when the capsule moves through the GT tract. Comparison with related image segmentation methods indicate we obtain better results in terms of agreement with expert ground-truth boundary markings.
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paper_title: Polyp detection in Wireless Capsule Endoscopy images by using region-based active contour model

paper_content:

Wireless Capsule Endoscopy (WCE) is an almost new technology to capture images from the whole of Gastrointestinal (GI) tract, noninvasively. WCE is a very useful technology to detect various abnormalities like blood based abnormalities, ulcers and polyps. We note that polyps are growing tissues occur on the surface of tissue instead of inside an organ. Most polyps are not cancerous but if one becomes larger than a centimeter, it can turn into cancer by great chance. So, one of the most important advantages of WCE can be the early detection of polyps and cancers. In this paper we proposed using region-based Active Contour Method (ACM) and geometric feature for automatic detection of polyps. The results on a set of images show that the proposed method can achieve %90.91 accuracy, %100 sensitivity and %83.33 specificity on our data set.
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paper_title: Towards a multimodal wireless video capsule for detection of colonic polyps as prevention of colorectal cancer

paper_content:

Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the identification of polyps. In this paper, a new multimodal embeddable method for polyp detection and classification in wireless capsule endoscopic images was developed and tested. The multimodal wireless capsule used both 2D and 3D data to identify possible polyps and to deliver cancerous information of the polyps based on 3D geometric features. Possible polyps within the image (2D) were extracted using simple geometric shape features and, in a second step, the candidate regions of interest (ROI) were evaluated with a boosting-based method using textural features. Once the 2D identification of polyps has been performed, the two-class (“malignant” or “begnin”) classification of the polyps is achieved using the 3D parameters computed from the preselected ROI using an active stereo vision system. At this stage, a Support Vector Machine (SVM) classifier is used to proceed to the final classification and to make possible a pre diagnosis. The new proposed multimodal approach based on 2D-3D feature extraction improves WCE capabilities to identify and classify polyps: The boosting-based polyp classification demonstrated a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8% on a database composed of 300 hundred positive examples and 1200 negative ones; Considering the 3D performance, a large scale demonstrator was evaluated and tested to perform in vitro experiments on an ad hoc polyp database. The performance of the 3D approach achieved a correct classification rate (malignant or benin) of approximately 95%.
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paper_title: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

paper_content:

Purpose Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested.Methods First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method’s performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself.Results The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images.Conclusion A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.
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paper_title: Towards real-time in situ polyp detection in WCE images using a boosting-based approach

paper_content:

This paper presents a new embeddable method for polyp detections in Wireless Capsule Endoscopic - WCE images. this approach consists first of extracting candidate polyps within the image using geometric considerations about related shape, and second, in classifying (polyp/non-polyp) obtained candidates by a boosting-based method using texture features. The proposed approach has been designed in accordance with the hardware constraints related to FPGA implementation for integration within WCE imaging device. The classification performance of the method have been evaluated on a large dataset of 300 polyps, and 1200 non-polyps images. Experiments show interesting and promising performance: the boosting-based classification is characterized by a sensitivity of 91%, a specificity of 95% and a false detection rate of 4.8%, the detection rate of the overall processing chain being of 68%. The performance of the boosting-based classification are in accordance with the most recent reference on this particular topic using the same dataset. Building of a dedicated WCE image database should permit the improvement of the global detection rate.
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paper_title: Automatic colon polyp detection in endoscopic capsule images

paper_content:

This paper presents a method of automatic detection of colon polyps in endoscopic capsule images. This approach uses color and geometrical analysis of the images and uses histogram of gradients features and multilayer perceptron neural networks for performing the detection at multiple scales. The detection performance is tested using a database of 30540 images, 540 of them containing at least one polyp.
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paper_title: Role of Enhanced Visibility in Evaluating Polyposis Syndromes Using a Newly Developed Contrast Image Capsule Endoscope

paper_content:

Background/Aims ::: A flexible spectral imaging color enhancement system was installed in new capsule software for video capsule endoscopy. Contrast image capsule endoscopy (CICE) is a novel technology using light-emitting diodes selected for the main absorption range of hemoglobin. We assessed the feasibility and diagnostic effi cacy for small bowel surveillance in patients with polyposis syndromes.
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paper_title: Color and Position versus Texture Features for Endoscopic Polyp Detection

paper_content:

This paper presents a comparison of texture based and color and position based methods for polyp detection in endoscopic video images. Two methods for texture feature extraction that presented good results in previous studies were implemented and their performance is compared against a simple combination of color and position features. Although this more simple approach produces a much higher number of features than the other approaches, a SVM with a KBF kernel is able to deal with this high dimensional input space and it turns out that it outperforms the previous approaches on the experiments performed in a database of 4620 images from endoscopic video.
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paper_title: Self-Stabilizing Colonic Capsule Endoscopy: Pilot Study of Acute Canine Models

paper_content:

Video capsule endoscopy (VCE) is a noninvasive method for examining the gastrointestinal tract which has been successful in small intestine studies. Recently, VCE has been attempted in the colon. However, the capsule often tumbles in the wider colonic lumen, resulting in missed regions. Self-stabilizing VCE is a novel method to visualize the colon without tumbling. The aim of the present study was to comparatively quantify the effect of stabilization of a commercially available nonmodified capsule endoscope (CE) MiroCam and its modified self-stabilizing version in acute canine experiments. Two customized MiroCam CEs were reduced in volume at the nonimaging back-end to allow the attachment of a self-expanding, biocompatible stabilizing device. Four mongrel dogs underwent laparotomy and exteriorization of a 15-cm segment of the proximal descending colon. A single CE, either self-stabilizing or nonmodified was inserted through an incision into the lumen of the colon followed by phar macologically induced colonic peristalsis. The inserted capsule was propelled distally through the colon and expelled naturally through the anus. Novel signal processing method was developed to quantify the video stabilization based on camera tracking a pre determined target point (locale). The average locale trajectory, the average radius movement of the locale, and the maximum rate of change of the locale for sequential images were significantly lower for the stabilized capsules compared to the nonstabilized ones (p <; 0.05). The feasibility of self-stabilized capsule endoscopy has been demonstrated in acute canine experiments.
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paper_title: Automatic Polyp Detection in Pillcam Colon 2 Capsule Images and Videos: Preliminary Feasibility Report

paper_content:

Background. The aim of this work is to present an automatic colorectal polyp detection scheme for capsule endoscopy. Methods. PillCam COLON2 capsule-based images and videos were used in our study. The database consists of full exam videos from five patients. The algorithm is based on the assumption that the polyps show up as a protrusion in the captured images and is expressed by means of a P-value, defined by geometrical features. Results. Seventeen PillCam COLON2 capsule videos are included, containing frames with polyps, flat lesions, diverticula, bubbles, and trash liquids. Polyps larger than 1 cm express a P-value higher than 2000, and 80% of the polyps show a P-value higher than 500. Diverticula, bubbles, trash liquids, and flat lesions were correctly interpreted by the algorithm as nonprotruding images. Conclusions. These preliminary results suggest that the proposed geometry-based polyp detection scheme works well, not only by allowing the detection of polyps but also by differentiating them from nonprotruding images found in the films.
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paper_title: Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps

paper_content:

We have developed a three-dimensional (3-D) computer-aided diagnosis scheme for automated detection of colonic polyps in computed tomography (CT) colonographic data sets, and assessed its performance based on colonoscopy as the gold standard. In this scheme, a thick region encompassing the entire colonic wall is extracted from an isotropic volume reconstructed from the CT images in CT colonography. Polyp candidates are detected by first computing of 3-D geometric features that characterize polyps, folds, and colonic walls at each voxel in the extracted colon, and then segmenting of connected components corresponding to suspicious regions by hysteresis thresholding based on these geometric features. We apply fuzzy clustering to these connected components to obtain the polyp candidates. False-positive (FP) detections are then reduced by computation of several 3-D volumetric features characterizing the internal structures of the polyp candidates, followed by the application of discriminant analysis to the feature space generated by these volumetric features. The locations of the polyps detected by our computerized method were compared to the gold standard of conventional colonoscopy. The performance was evaluated based on 43 clinical cases, including 12 polyps determined by colonoscopy. Our computerized scheme was shown to have the potential to detect polyps in CT colonography with a clinically acceptable high sensitivity and a low FP rate.
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paper_title: Segmentation and detection of colorectal polyps using local polynomial approximation

paper_content:

In this paper we introduce a new methodology to segment and detect colorectal polyps in endoscopic images obtained by a wireless capsule endoscopic device. The cornerstone of our approach is the fact that polyps are protrusions emerging from colonic walls. Thus, they can be segmented by simple curvature descriptors. Curvature is based on derivatives, thus very sensitive to noise and image artifacts. Furthermore, the acquired images are sampled on a grid which further complicates the computation of derivatives. To cope with these degradation mechanisms, we use use Local Polynomial Approximation, which, simultaneously, denoise the observed images and provides a continuous representation suitable to compute derivatives. On the top of the image segmentation, we built a support vector machine to classify the segmented regions as polyps or non-polyps. The features used in the classifier are selected with a wrapper selection algorithm (greedy forward feature selection algorithm with support vector machines). The proposed segmentation and detection methodology is tested in several scenarios presenting very good results both using the same video sequences as training data and testing data (cross-feature validation) and different video sequences as training and testing data.
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paper_title: Colorectal Polyps Detection Using Texture Features and Support Vector Machine

paper_content:

In this paper we propose a novel method in detecting colorectal polyps on colonoscopic images. Texture features are applied in polyps and normal tissues training and classification. Support vector machine is used as a classifier to identify the position of polyps. Seventy-four colonoscopic images are collected to test the system. Half of them are used as training images and half are used as testing. The experimental result shows the system can identify all polyps if the colonoscopic images contain single polyp. The sensitivity is 86.2% and the false-positive rate is 1.26 mark per-image.
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paper_title: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

paper_content:

Purpose Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested.Methods First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method’s performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself.Results The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images.Conclusion A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.
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paper_title: Polyp Detection via Imbalanced Learning and Discriminative Feature Learning

paper_content:

Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically challenging due to the diversity of polyp types, expensive inspection, and labor-intensive labeling tasks. For this reason, the polyp datasets usually tend to be imbalanced, i.e., the number of non-polyp samples is much larger than that of polyp samples, and learning with those imbalanced datasets results in a detector biased toward a non-polyp class. In this paper, we propose a data sampling-based boosting framework to learn an unbiased polyp detector from the imbalanced datasets. In our learning scheme, we learn multiple weak classifiers with the datasets rebalanced by up/down sampling, and generate a polyp detector by combining them. In addition, for enhancing discriminability between polyps and non-polyps that have similar appearances, we propose an effective feature learning method using partial least square analysis, and use it for learning compact and discriminative features. Experimental results using challenging datasets show obvious performance improvement over other detectors. We further prove effectiveness and usefulness of the proposed methods with extensive evaluation.
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paper_title: Deep sparse feature selection for computer aided endoscopy diagnosis

paper_content:

In this paper, we develop a computer aided diagnosis algorithm to detect and classify the abnormalities in vision-based endoscopic examination. We focus on analyzing the traditional gastroscope data and help the medical experts improve the accuracy of medical diagnosis with our analysis tool. To achieve this, we first segment the image into superpixels, then extract various color and texture features from them and combine the features into one feature vector to represent the images. This approach is more flexible and accurate than the traditional patch-based image representation. Then we design a novel feature selection model with group sparsity, Deep Sparse SVM (DSSVM) that not only can assign a suitable weight to the feature dimensions like the other traditional feature selection models, but also directly exclude useless features from the feature pool. Thus, our DSSVM model can maintain the accuracy while reducing the computation complexity. Moreover, the image quality is also pre-assessed. For the experiments, we build a new gastroscope dataset with a total of about 3800 images from 1284 volunteers, and conducted various experiments and comparisons with other algorithms to justify the effectiveness and efficiency of our algorithm. HighlightsWe design a new feature selection model via group sparsity, named Deep Sparse SVM.We design a general computer aided endoscopy diagnosis framework.We build a new endoscopy dataset including 10k+ images from 1284 volunteers.We annotate 3.8k+ images with pixel-level and frame-level groundtruth.
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paper_title: Smart Videocapsule for Early Diagnosis of Colorectal Cancer: Toward Embedded Image Analysis

paper_content:

Wireless capsule endoscopy (WCE) enables screening of the gastrointestinal tract by a swallowable imaging system. However, contemporary WCE systems have several limitations—battery, low processing capabilities, among others—which often result in low diagnostic yield. In this chapter, after a technical presentation of the components of a standard WCE, the authors discuss the related limitations and introduce a new concept of smart capsule with embedded image processing capabilities based on a boosting approach using textural features. We discuss the feasibility of the hardware integration of the detection–recognition method, also with respect to the most recent FPGA technologies.
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paper_title: Lesion detection of endoscopy images based on convolutional neural network features

paper_content:

Since gastroscopy is able to observe the interior of gastrointestinal tract directly, it has been widely used for gastrointestinal examination. But it is hard for clinicians to accurately detect gastrointestinal disease due to its great dependence on doctors experiences. Therefore, a computer-aided lesion detection system can offer great help for clinicians. In this paper, we propose a new scheme for endoscopy image lesion detection. A trainable feature extractor based on convolutional neural network (CNN) is utilized to get more generic features for endoscopy images. And features are fed to support vector machine (SVM) to enhance the generalization ability. Experiments show that the proposed scheme outperforms the previous conventional methods based on color and texture features.
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paper_title: Shape estimation of gastrointestinal polyps using motion information

paper_content:

Polyp size quanti cation is currently the main variable for deciding the patient treatment during an endoscopic procedure. Nowadays, the polyp size is estimated by an expert, even when using devices that are provided with calibrated grids. As such estimation is highly subjective, automatic approaches have come to be appealing but also challenging because the polyp shape and appearance variability, the di erent types of motion present during the capture and the specular highlight noise. This work presents a novel approach to automatically estimate gastrointestinal polyp shapes in a video endoscopic sequence using spatiotemporal information. For doing so, a local spatio temporal descriptor is built up to obtain an initial segmentation since the polyp is the region with more movement. Then, an initial polyp manual segmentation outlines a region of interest (RoI) in the rst frame of the sequence and used as a reference for the polyp tracking during the sequence. Afterward, an exhaustive cross-correlation of the initial shape is carried out along the sequence and fused with the motion descriptor to re ne the original segmentation. The proposed approach was evaluated in 15 real video sequences achieving an average DSC score of 0:67% .
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Title: Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review

Section 1: Introduction
Description 1: Introduce the basics of Video Capsule Endoscopy (VCE) and the significance of polyp detection. Discuss the challenges and necessity of automated detection and segmentation.

Section 2: Review of polyp detection and segmentation in VCE
Description 2: Provide a comprehensive review of the existing techniques for polyp detection and segmentation in VCE. Discuss the two main categories: polyp detection and polyp segmentation, and elaborate on methods and key techniques from the literature.

Section 3: Polyp detection in capsule endoscopy videos
Description 3: Detail the methods and approaches used specifically for detecting polyps in capsule endoscopy videos, including different classifiers and techniques. Summarize findings and tabulate the performance metrics of reviewed methods.

Section 4: Polyp localization or segmentation within a VCE frame
Description 4: Discuss methods for localizing or segmenting polyps within a single VCE frame. Explain various approaches and their effectiveness, classified based on the techniques employed.

Section 5: Holistic systems
Description 5: Explore integrated systems that combine multiple approaches for polyp detection and segmentation, and discuss the current state and limitations of these holistic systems.

Section 6: Discussion and outlook
Description 6: Reflect on the current advancements, challenges, and future directions in polyp detection and segmentation from VCE, incorporating deep learning and other emerging technologies.