A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video
---
paper_title: Capsule Endoscopy: From Current Achievements to Open Challenges

paper_content:

Wireless capsule endoscopy (WCE) can be considered an example of disruptive technology since it represents an appealing alternative to traditional diagnostic techniques. This technology enables inspection of the digestive system without discomfort or need for sedation, thus preventing the risks of conventional endoscopy, and has the potential of encouraging patients to undergo gastrointestinal (GI) tract examinations. However, currently available clinical products are passive devices whose locomotion is driven by natural peristalsis, with the drawback of failing to capture the images of important GI tract regions, since the doctor is unable to control the capsule's motion and orientation. To address these limitations, many research groups are working to develop active locomotion devices that allow capsule endoscopy to be performed in a totally controlled manner. This would enable the doctor to steer the capsule towards interesting pathological areas and to accomplish medical tasks. This review presents a research update on WCE and describes the state of the art of the basic modules of current swallowable devices, together with a perspective on WCE potential for screening, diagnostic, and therapeutic endoscopic procedures.
---
paper_title: Colour spaces: perceptual, historical and applicational background

paper_content:

In this paper, we present and overview of colour spaces used in electrical engineering and image processing. We stress the importance of the perceptual, historical and applicational background that led to a colour space. The colour spaces presented are: RGB, opponent-colour spaces, phenomenal colour spaces, CMY, CMYK, TV colour spaces (YUV and YIQ), PhotoYCC, CIE XYZ, Lab and Luv colour spaces.
---
paper_title: Textural Features for Image Classification

paper_content:

Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications.
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paper_title: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters.

paper_content:

Two-dimensional spatial linear filters are constrained by general uncertainty relations that limit their attainable information resolution for orientation, spatial frequency, and two-dimensional (2D) spatial position. The theoretical lower limit for the joint entropy, or uncertainty, of these variables is achieved by an optimal 2D filter family whose spatial weighting functions are generated by exponentiated bivariate second-order polynomials with complex coefficients, the elliptic generalization of the one-dimensional elementary functions proposed in Gabor's famous theory of communication [J. Inst. Electr. Eng. 93, 429 (1946)]. The set includes filters with various orientation bandwidths, spatial-frequency bandwidths, and spatial dimensions, favoring the extraction of various kinds of information from an image. Each such filter occupies an irreducible quantal volume (corresponding to an independent datum) in a four-dimensional information hyperspace whose axes are interpretable as 2D visual space, orientation, and spatial frequency, and thus such a filter set could subserve an optimally efficient sampling of these variables. Evidence is presented that the 2D receptive-field profiles of simple cells in mammalian visual cortex are well described by members of this optimal 2D filter family, and thus such visual neurons could be said to optimize the general uncertainty relations for joint 2D-spatial-2D-spectral information resolution. The variety of their receptive-field dimensions and orientation and spatial-frequency bandwidths, and the correlations among these, reveal several underlying constraints, particularly in width/length aspect ratio and principal axis organization, suggesting a polar division of labor in occupying the quantal volumes of information hyperspace.(ABSTRACT TRUNCATED AT 250 WORDS)
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paper_title: Developing assessment system for wireless capsule endoscopy videos based on event detection

paper_content:

Along with the advancing of technology in wireless and miniature camera, Wireless Capsule Endoscopy (WCE), the ::: combination of both, enables a physician to diagnose patient's digestive system without actually perform a surgical ::: procedure. Although WCE is a technical breakthrough that allows physicians to visualize the entire small bowel ::: noninvasively, the video viewing time takes 1 - 2 hours. This is very time consuming for the gastroenterologist. Not ::: only it sets a limit on the wide application of this technology but also it incurs considerable amount of cost. Therefore, it ::: is important to automate such process so that the medical clinicians only focus on interested events. As an extension ::: from our previous work that characterizes the motility of digestive tract in WCE videos, we propose a new assessment ::: system for energy based events detection (EG-EBD) to classify the events in WCE videos. For the system, we first ::: extract general features of a WCE video that can characterize the intestinal contractions in digestive organs. Then, the ::: event boundaries are identified by using High Frequency Content (HFC) function. The segments are classified into WCE ::: event by special features. In this system, we focus on entering duodenum, entering cecum, and active bleeding. This ::: assessment system can be easily extended to discover more WCE events, such as detailed organ segmentation and more ::: diseases, by using new special features. In addition, the system provides a score for every WCE image for each event. ::: Using the event scores, the system helps a specialist to speedup the diagnosis process.
---
paper_title: Wireless Capsule Endoscopy Color Video Segmentation

paper_content:

This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses ldquopill-camrdquo technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.
---
paper_title: Detection of contractions in adaptive transit time of the small bowel from wireless capsule endoscopy videos

paper_content:

Recognizing intestinal contractions from wireless capsule endoscopy (WCE) image sequences provides a non-invasive method of measurement, and suggests a solution to the problems of traditional techniques for assessing intestinal motility. Based on the characteristics of contractile patterns and information on their frequencies, the contractions can be investigated using essential image features extracted from WCE videos. In this study, we proposed a coherent three-stage procedure using temporal and spatial features. The possible contractions are recognized by changes in the edge structure of the intestinal folds in Stage 1 and evaluating similarity features in consecutive frames in Stage 2. In order to take account of the properties of contraction frequency, we consider that the possible contractions are located within windows including consecutive frames. The size of these contraction windows is adjusted according to the passage of the WCE. These procedures aim to exclude as many non-contractions as possible. True contractions are determined through spatial analysis of directional information in Stage 3. Using the proposed method, 81% of true contractions are detected with a 37% false alarm rate for evaluations in the experiments. The overall performance of this method is better than that of previous methods, in terms of both the quality and quantity indices. The results suggest feasible data for further clinical applications.
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paper_title: Detection of bleeding patterns in WCE video using multiple features

paper_content:

Wireless capsule endoscopy (WCE) uses a pill size capsule which is easily swallowed by patients, that comes with wireless power transmission. WCE images are recorded into a hard disk to be later analyzed by a doctor. This paper studies various bleeding characteristics and proposes a computer- based procedure to reduce the analysis time for detecting suspected bleeding diseases in patients. A two-step process is proposed, the first to discriminate images with or without 'bleeding', using a block-based color saturation method. The second step, to refine the initial classification and increase its reliability uses a pixel-based saturation-luminance analysis. The luminance-saturation relationship is explored to reduce the false positive detections resulting from Step 1. Another analysis tool is proposed, exploring the red color component, to analyze the presence of food remains or bubbles, partly occluding the tissues. Experimental results suggest that bleeding patterns can be effectively detected using the proposed method, to guide the specialist to images with bleeding patterns. This proposal is added into the previously proposed framework, Capsule Endoscopy Supporting Software (CESS), consisting of multiple tools to analyze WCE images.
---
paper_title: Ulcer detection in wireless capsule endoscopy video

paper_content:

Wireless Capsule Endoscopy (WCE) is a painless and noninvasive technique that allows physicians to visualize the entire small bowel. Before WCE was introduced, examining the entire small bowel was impossible without surgical procedure. Although WCE is a technology breakthrough, the manual video diagnosis session is time consuming and is prone to human cognition errors. Therefore, gastroenterologists urge computer vision researchers to develop computer-aided diagnosis systems to assist the review session. In this paper, we would like to present an ulcer detection scheme that identifies ulcers in WCE video. A saliency map is created by means of channel mixer to highlight suspicious regions. Once the suspicious regions are identified, we extract textural features along the contour and classify them using a trained classifier for ulcer validation.
---
paper_title: Identification of ulcers in Wireless Capsule Endoscopy videos

paper_content:

Wireless Capsule Endoscopy (WCE) is a non invasive procedure which is used to view the lower gastrointestinal tract. Physicians can detect diseases such as bleeding, Crohn's disease, peptic ulcers, and colon cancer. In this paper a methodology is presented to identify peptic ulcers in the small intestine automatically. It first performs color transformation into the HSV color space; it utilizes log Gabor filters to find meaningful regions. A segmentation scheme is used to extract color information of these meaningful regions in the original RGB color space. Additionally, texture information is extracted and together with color values are fed into an artificial neural network for classification. Illustrative results from the methodology are also given in this paper.
---
paper_title: Developing assessment system for wireless capsule endoscopy videos based on event detection

paper_content:

Along with the advancing of technology in wireless and miniature camera, Wireless Capsule Endoscopy (WCE), the ::: combination of both, enables a physician to diagnose patient's digestive system without actually perform a surgical ::: procedure. Although WCE is a technical breakthrough that allows physicians to visualize the entire small bowel ::: noninvasively, the video viewing time takes 1 - 2 hours. This is very time consuming for the gastroenterologist. Not ::: only it sets a limit on the wide application of this technology but also it incurs considerable amount of cost. Therefore, it ::: is important to automate such process so that the medical clinicians only focus on interested events. As an extension ::: from our previous work that characterizes the motility of digestive tract in WCE videos, we propose a new assessment ::: system for energy based events detection (EG-EBD) to classify the events in WCE videos. For the system, we first ::: extract general features of a WCE video that can characterize the intestinal contractions in digestive organs. Then, the ::: event boundaries are identified by using High Frequency Content (HFC) function. The segments are classified into WCE ::: event by special features. In this system, we focus on entering duodenum, entering cecum, and active bleeding. This ::: assessment system can be easily extended to discover more WCE events, such as detailed organ segmentation and more ::: diseases, by using new special features. In addition, the system provides a score for every WCE image for each event. ::: Using the event scores, the system helps a specialist to speedup the diagnosis process.
---
paper_title: Tumor recognition in endoscopic video images using artificial neural network architectures

paper_content:

The paper focuses on a scheme for automated tumor recognition using images acquired during endoscopic sessions. The proposed recognition system is based on multilayer feed forward neural networks (MFNNs) and uses texture information encoded with corresponding statistical measures that are fed as input to the MFNN. Experiments were performed for recognition of different types of tumors in various images and also a number of sequentially acquired frames. The recognition of a polypoid tumor of the colon in the original image, which were used for training was very high. The trained network was also able to satisfactorily recognize the tumor in a sequence of video frames. The results of the proposed approach were very promising and it seems that it can be efficiently applied for tumor recognition.
---
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.
---
paper_title: A comparative study of shape features for polyp detection in wireless capsule endoscopy images

paper_content:

Wireless capsule endoscopy (WCE) has been gradually employed in hospitals because it can directly view the entire small bowel of a human body for the first time. However, a troublesome problem related to this new technology is that too many images produced by WCE will take a lot of efforts for doctors to inspect. In this paper, we propose a comparative study of shape features aiming for intestinal polyp detection for WCE images. As polyps exhibit strong shape characteristics, also a powerful clue used by physicians, we investigate two kinds of shape features, MEPG-7 region-based shape descriptor and Zernike moments, in our study. With multi-layer perceptron neural network as the classifier, experiments on our present image data show that it is promising to employ both Zernike moments and MEPG-7 region-based shape descriptor as the shape features to recognize the intestinal polyp regions, and a better performance is obtained by the Zernike moments based shape features.
---
paper_title: Texture analysis for ulcer detection in capsule endoscopy images

paper_content:

Capsule endoscopy (CE) has gradually seen its wide application in hospitals in the last few years because it can view the entire small bowel without invasiveness. However, CE produces too many images each time, thus causing a huge burden to physicians, so it is meaningful to help clinicians if we can employ computerized methods to diagnose. This paper presents a new texture extraction scheme for ulcer region discrimination in CE images. A new idea of curvelet based local binary pattern is proposed as textural features to distinguish ulcer regions from normal regions, which makes full use of curvelet transformation and local binary pattern. The proposed new textural features can capture multi-directional features and show robustness to illumination changes. Extensive classification experiments using multilayer perceptron neural network and support vector machines on our image data validate that it is promising to employ the proposed texture features to recognize ulcer regions in CE images.
---
paper_title: Obscure bleeding detection in endoscopy images using support vector machines

paper_content:

Wireless capsule endoscopy (WCE) is a recently established imaging technology that requires no wired device intrusion and can be used to examine the entire small intestine non-invasively. Determining bleeding signs out of over 55,000 WCE images is a tedious and expensive job by human reviewing. Our goal is to develop an automatic obscure bleeding detection method by employing image color features and support vector machine (SVM) classifier. The bleeding lesion detection problem is a binary classification problem. We use SVMs for this problem and a new feature selection procedure is proposed. Our experiments show that SVM can be very efficient in processing unseen instances and may yield very high accuracy rate, in particular with our new proposed feature selection. More specifically, for this bleeding detection problem, training an SVM with 640 samples can be completed in as little as 0.01 second on a Dell workstation with dual Xeon CPUs, and classifying an image using the trained SVM can be done in as little as 0.03 milliseconds. The accuracy for both sensitivity and specificity can be over 99%.
---
paper_title: Ulcer recognition in capsule endoscopy images by texture features

paper_content:

The capsule endoscopy (CE) has gradually found its applications in hospitals because of its great advantage that it can view the entire small bowel with almost no invasiveness compared to traditional endoscopes and other imaging techniques for gastrointestinal diseases. However, a major issue with this new technology is that too many images produced by CE causes a huge burden to physicians, so it is very meaningful to help the clinicians if we could partially implement computer aided diagnosis. We proposed a new scheme aimed for ulcer region discrimination in CE images in this paper. This new scheme utilizes texture feature, a very powerful clue for the physicians to diagnose, to recognize ulcer regions with neural network classifier. We advance a new idea of curvelet based local binary pattern as the textural features to discriminate ulcer regions from normal regions, which make full use of the curvelet transform and uniform local binary pattern. Experiments on our present image data sets validate that it is promising to employ the proposed texture features to recognize the ulcer regions.
---
paper_title: Computer-Aided Detection of Bleeding Regions for Capsule Endoscopy Images

paper_content:

Capsule endoscopy (CE) has been widely used to diagnose diseases in human digestive tract. However, a tough problem of this new technology is that too many images to be inspected by eyes cause a huge burden to physicians, so it is significant to investigate computerized diagnosis methods. In this paper, a new computer-aided system aimed for bleeding region detection in CE images is proposed. This new system exploits color texture feature, an important clue used by physicians, to analyze status of gastrointestinal tract. We put forward a new idea of chrominance moment as the color part of color texture feature, which makes full use of Tchebichef polynomials and illumination invariant of hue/saturation/intensity color space. Combined with uniform local binary pattern, a current texture representation model, it can be applied to discriminate normal regions and bleeding regions in CE images. Classification of bleeding regions using multilayer perceptron neural network is then deployed to verify performance of the proposed color texture features. Experimental results on our bleeding image data show that the proposed scheme is promising in detecting bleeding regions.
---
paper_title: Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine

paper_content:

Wireless capsule endoscopy (WCE) has been gradually applied in hospitals due to its great advantage that it can directly view the entire small bowel in human body compared with traditional endoscopies and other imaging techniques for gastrointestinal diseases. However, a challenging problem with this new technology is that too many images produced by WCE causes a tough task to doctors, so it is very significant to help and relief the clinicians if we can develop computer based automatic detection system to prescreen the collected large amount of images and identify the images with potential problems. In this paper, we propose a new scheme aimed for small bowel tumor detection of WCE images. This new scheme utilizes texture feature, also a powerful clue used by physicians, to detect tumor images with support vector machine. We put forward a new idea of wavelet based local binary pattern as the textural features to discriminate tumor regions from normal regions, which take advantage of wavelet transform and uniform local binary pattern. With support vector machine as the classifier, three-fold cross validation experiments on our present image data verify that it is promising to employ the proposed texture features to recognize the small bowel tumor regions.
---
paper_title: A methodology for detecting blood-based abnormalities in Wireless Capsule Endoscopy videos

paper_content:

This paper deals with a novel methodology for automatically detecting blood based abnormalities (bleeding, angioectasia and erythema) in wireless capsule endoscopy videos. The methodology is based on a synergistic integration of methods, such as Color K-L transformation, fuzzy region segmentation, Local-Global graphs. The methodology presents several unique features that separate it from the classical bleeding based methods. These features are mainly based on (i) the classification of the blood based pathological cases in three major categories approved by gastroenterologists; (ii) it is based on a synergy of methods tested in the research field for their robustness. Illustrative examples from the methodology are also presented in this paper.
---
paper_title: Automatic detection of small bowel tumors in capsule endoscopy based on color curvelet covariance statistical texture descriptors

paper_content:

Traditional endoscopic methods do not allow the visualization of the entire Gastrointestinal (GI) tract. Wireless Capsule Endoscopy (CE) is a diagnostic procedure that overcomes this limitation of the traditional endoscopic methods. The CE video frames possess rich information about the condition of the stomach and intestine mucosa, encoded as color and texture patterns. It is known for a long time that human perception of texture is based in a multi-scale analysis of patterns, which can be modeled by multi-resolution approaches. Furthermore, modeling the covariance of textural descriptors has been successfully used in classification of colonoscopy videos. Therefore, in the present paper it is proposed a frame classification scheme based on statistical textural descriptors taken from the Discrete Curvelet Transform (DCT) domain, a recent multi-resolution mathematical tool. The DCT is based on an anisotropic notion of scale and high directional sensitivity in multiple directions, being therefore suited to characterization of complex patterns as texture. The covariance of texture descriptors taken at a given detail level, in different angles, is used as classification feature, in a scheme designated as Color Curvelet Covariance. The classification step is performed by a multilayer perceptron neural network. The proposed method has been applied in real data taken from several capsule endoscopic exams and reaches 97.2% of sensitivity and 97.4% specificity. These promising results support the feasibility of the proposed method.
---
paper_title: Identification of ulcers in Wireless Capsule Endoscopy videos

paper_content:

Wireless Capsule Endoscopy (WCE) is a non invasive procedure which is used to view the lower gastrointestinal tract. Physicians can detect diseases such as bleeding, Crohn's disease, peptic ulcers, and colon cancer. In this paper a methodology is presented to identify peptic ulcers in the small intestine automatically. It first performs color transformation into the HSV color space; it utilizes log Gabor filters to find meaningful regions. A segmentation scheme is used to extract color information of these meaningful regions in the original RGB color space. Additionally, texture information is extracted and together with color values are fed into an artificial neural network for classification. Illustrative results from the methodology are also given in this paper.
---
paper_title: Developing assessment system for wireless capsule endoscopy videos based on event detection

paper_content:

Along with the advancing of technology in wireless and miniature camera, Wireless Capsule Endoscopy (WCE), the ::: combination of both, enables a physician to diagnose patient's digestive system without actually perform a surgical ::: procedure. Although WCE is a technical breakthrough that allows physicians to visualize the entire small bowel ::: noninvasively, the video viewing time takes 1 - 2 hours. This is very time consuming for the gastroenterologist. Not ::: only it sets a limit on the wide application of this technology but also it incurs considerable amount of cost. Therefore, it ::: is important to automate such process so that the medical clinicians only focus on interested events. As an extension ::: from our previous work that characterizes the motility of digestive tract in WCE videos, we propose a new assessment ::: system for energy based events detection (EG-EBD) to classify the events in WCE videos. For the system, we first ::: extract general features of a WCE video that can characterize the intestinal contractions in digestive organs. Then, the ::: event boundaries are identified by using High Frequency Content (HFC) function. The segments are classified into WCE ::: event by special features. In this system, we focus on entering duodenum, entering cecum, and active bleeding. This ::: assessment system can be easily extended to discover more WCE events, such as detailed organ segmentation and more ::: diseases, by using new special features. In addition, the system provides a score for every WCE image for each event. ::: Using the event scores, the system helps a specialist to speedup the diagnosis process.
---
paper_title: Tumor recognition in endoscopic video images using artificial neural network architectures

paper_content:

The paper focuses on a scheme for automated tumor recognition using images acquired during endoscopic sessions. The proposed recognition system is based on multilayer feed forward neural networks (MFNNs) and uses texture information encoded with corresponding statistical measures that are fed as input to the MFNN. Experiments were performed for recognition of different types of tumors in various images and also a number of sequentially acquired frames. The recognition of a polypoid tumor of the colon in the original image, which were used for training was very high. The trained network was also able to satisfactorily recognize the tumor in a sequence of video frames. The results of the proposed approach were very promising and it seems that it can be efficiently applied for tumor recognition.
---
paper_title: Wireless Capsule Endoscopy Color Video Segmentation

paper_content:

This paper describes the use of color image analysis to automatically discriminate between oesophagus, stomach, small intestine, and colon tissue in wireless capsule endoscopy (WCE). WCE uses ldquopill-camrdquo technology to recover color video imagery from the entire gastrointestinal tract. Accurately reviewing and reporting this data is a vital part of the examination, but it is tedious and time consuming. Automatic image analysis tools play an important role in supporting the clinician and speeding up this process. Our approach first divides the WCE image into subimages and rejects all subimages in which tissue is not clearly visible. We then create a feature vector combining color, texture, and motion information of the entire image and valid subimages. Color features are derived from hue saturation histograms, compressed using a hybrid transform, incorporating the discrete cosine transform and principal component analysis. A second feature combining color and texture information is derived using local binary patterns. The video is segmented into meaningful parts using support vector or multivariate Gaussian classifiers built within the framework of a hidden Markov model. We present experimental results that demonstrate the effectiveness of this method.
---
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.
---
paper_title: A comparative study of shape features for polyp detection in wireless capsule endoscopy images

paper_content:

Wireless capsule endoscopy (WCE) has been gradually employed in hospitals because it can directly view the entire small bowel of a human body for the first time. However, a troublesome problem related to this new technology is that too many images produced by WCE will take a lot of efforts for doctors to inspect. In this paper, we propose a comparative study of shape features aiming for intestinal polyp detection for WCE images. As polyps exhibit strong shape characteristics, also a powerful clue used by physicians, we investigate two kinds of shape features, MEPG-7 region-based shape descriptor and Zernike moments, in our study. With multi-layer perceptron neural network as the classifier, experiments on our present image data show that it is promising to employ both Zernike moments and MEPG-7 region-based shape descriptor as the shape features to recognize the intestinal polyp regions, and a better performance is obtained by the Zernike moments based shape features.
---
paper_title: A comparative study of texture features for the discrimination of gastric polyps in endoscopic video

paper_content:

In this paper, we extend the application of four texture feature extraction methods proposed for the detection of colorectal lesions, into the discrimination of gastric polyps in endoscopic video. Support Vector Machines have been utilized for the texture classification task. The polyp discrimination performance of the surveyed schemes is compared by means of Receiver Operating Characteristics (ROC). The results advocate the feasibility of a computer-based system for polyp detection in video gastroscopy that exploits the textural characteristics of the gastric mucosa in conjunction with its color appearance.
---
paper_title: Texture analysis for ulcer detection in capsule endoscopy images

paper_content:

Capsule endoscopy (CE) has gradually seen its wide application in hospitals in the last few years because it can view the entire small bowel without invasiveness. However, CE produces too many images each time, thus causing a huge burden to physicians, so it is meaningful to help clinicians if we can employ computerized methods to diagnose. This paper presents a new texture extraction scheme for ulcer region discrimination in CE images. A new idea of curvelet based local binary pattern is proposed as textural features to distinguish ulcer regions from normal regions, which makes full use of curvelet transformation and local binary pattern. The proposed new textural features can capture multi-directional features and show robustness to illumination changes. Extensive classification experiments using multilayer perceptron neural network and support vector machines on our image data validate that it is promising to employ the proposed texture features to recognize ulcer regions in CE images.
---
paper_title: Computer-based detection of bleeding and ulcer in wireless capsule endoscopy images by chromaticity moments

paper_content:

The wireless capsule endoscopy (WCE) invented by Given Imaging has been gradually used in hospitals due to its great breakthrough that it can view the entire small bowel for gastrointestinal diseases. However, a tough problem associated with this new technology is that too many images to be examined by eyes cause a huge burden to physicians, so it is significant if we can help physicians do diagnosis using computerized methods. In this paper, a new method aimed for bleeding and ulcer detection in WCE images is proposed. This new approach mainly focuses on color feature, also a very powerful clue used by physicians for diagnosis, to judge the status of gastrointestinal tract. We propose a new idea of chromaticity moment as the features to discriminate normal regions and abnormal regions, which make full use of the Tchebichef polynomials and the illumination invariant of HSI color space, and we verify performances of the proposed features by employing neural network classifier. Experimental results on our present image data of bleeding and ulcer show that it is feasible to exploit the proposed chromaticity moments to detect bleeding and ulcer for WCE images.
---
paper_title: Ulcer recognition in capsule endoscopy images by texture features

paper_content:

The capsule endoscopy (CE) has gradually found its applications in hospitals because of its great advantage that it can view the entire small bowel with almost no invasiveness compared to traditional endoscopes and other imaging techniques for gastrointestinal diseases. However, a major issue with this new technology is that too many images produced by CE causes a huge burden to physicians, so it is very meaningful to help the clinicians if we could partially implement computer aided diagnosis. We proposed a new scheme aimed for ulcer region discrimination in CE images in this paper. This new scheme utilizes texture feature, a very powerful clue for the physicians to diagnose, to recognize ulcer regions with neural network classifier. We advance a new idea of curvelet based local binary pattern as the textural features to discriminate ulcer regions from normal regions, which make full use of the curvelet transform and uniform local binary pattern. Experiments on our present image data sets validate that it is promising to employ the proposed texture features to recognize the ulcer regions.
---
paper_title: Bleeding detection from capsule endoscopy videos

paper_content:

Reviewing medical videos for the presence of disease signs presents a unique problem to the conventional image classification tasks. The learning process based on imbalanced data set is heavily biased and tends to result in low sensitivity. In this article, we present a classification method for finding video frames that contain bleeding lesions. Our method re-balances the training samples by over-sampling the minority class and under-sampling the majority class. An SVM ensemble is then constructed using re-balanced data of three kinds of image features. Five sets of image frames were used in our experiments, each of which contains approximately 55,000 images and the ratio of minority and majority class is about 1:145. Our preliminary results demonstrated superior performance in sensitivity and comparative subjectivity with slight improvement.
---
paper_title: Detection of small bowel tumors in capsule endoscopy frames using texture analysis based on the discrete wavelet transform

paper_content:

Capsule endoscopy is an important tool to diagnose tumor lesions in the small bowel. The capsule endoscopic images possess vital information expressed by color and texture. This paper presents an approach based in the textural analysis of the different color channels, using the wavelet transform to select the bands with the most significant texture information. A new image is then synthesized from the selected wavelet bands, trough the inverse wavelet transform. The features of each image are based on second-order textural information, and they are used in a classification scheme using a multilayer perceptron neural network. The proposed methodology has been applied in real data taken from capsule endoscopic exams and reached 98.7% sensibility and 96.6% specificity. These results support the feasibility of the proposed algorithm.
---
paper_title: Small bowel tumor detection for wireless capsule endoscopy images using textural features and support vector machine

paper_content:

Wireless capsule endoscopy (WCE) has been gradually applied in hospitals due to its great advantage that it can directly view the entire small bowel in human body compared with traditional endoscopies and other imaging techniques for gastrointestinal diseases. However, a challenging problem with this new technology is that too many images produced by WCE causes a tough task to doctors, so it is very significant to help and relief the clinicians if we can develop computer based automatic detection system to prescreen the collected large amount of images and identify the images with potential problems. In this paper, we propose a new scheme aimed for small bowel tumor detection of WCE images. This new scheme utilizes texture feature, also a powerful clue used by physicians, to detect tumor images with support vector machine. We put forward a new idea of wavelet based local binary pattern as the textural features to discriminate tumor regions from normal regions, which take advantage of wavelet transform and uniform local binary pattern. With support vector machine as the classifier, three-fold cross validation experiments on our present image data verify that it is promising to employ the proposed texture features to recognize the small bowel tumor regions.
---

Title: A Review of Machine-Vision-Based Analysis of Wireless Capsule Endoscopy Video

Section 1: Introduction
Description 1: Introduce the concept of Wireless Capsule Endoscopy (WCE), its significance, and the issues that machine vision aims to solve.

Section 2: Image Features for Abstraction
Description 2: Discuss different image features used for abstraction in WCE video such as color, texture, and shape features.

Section 3: Color
Description 3: Describe how color is used in WCE image analysis, detailing different color models and their applications.

Section 4: Texture
Description 4: Explain the role of texture in analyzing WCE images, including common methods and challenges in texture feature extraction.

Section 5: Shape
Description 5: Outline the importance of shape features in WCE, particularly in the detection of polyps and tumors.

Section 6: Significant Medical Event Detection
Description 6: Cover the detection of significant medical events such as bleeding, ulcers, polyps, and tumors using machine vision techniques.

Section 7: Discussion
Description 7: Provide a discussion on the challenges and effectiveness of different features and methods used in machine vision for WCE.

Section 8: Conclusion
Description 8: Summarize the review, emphasizing the current state, challenges, and future prospects in machine-vision-based analysis of WCE videos.