Survey on liver CT image segmentation methods
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paper_title: Interactive Live-Wire Boundary Extraction

paper_content:

Abstract Live-wire segmentation is a new interactive tool for efficient, accurate and reproducible boundary extraction which requires minimal user input with a mouse. Optimal boundaries are computed and selected at interactive rates as the user moves the mouse starting from a manually specified seed point. When the mouse position comes into the proximity of an object edge, a ‘live-wire’ boundary snaps to, and wraps around the object of interest. The input of a new seed point ‘freezes’ the selected boundary segment and the process is repeated until the boundary is complete. Two novel enhancements to the basic live-wire methodology include boundary cooling and on-the-fly training. Data-driven boundary cooling generates seed points automatically and further reduces user input. On-the-fly training adapts the dynamic boundary to edges of current interest. Using the live-wire technique, boundaries are extracted in one-fifth of the time required for manual tracing, but with 4.4 times greater accuracy and 4.8 times greater reproducibility. In particular, interobserver reproducibility using the live-wire tool is 3.8 times greater than intraobserver reproducibility using manual tracing.
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paper_title: Liver Segmentation in CT Data: A Segmentation Refinement Approach

paper_content:

A stretchable knitted article of predominately terry loop construction, e.g., an athletic headband, with a design printed thereon, includes a knitted fabric of single jersey construction the reverse side of which has a predominant terry loop region bordering an integrally knit smaller reverse-side plain jersey region. The plain jersey region is oriented in the article for coursewise stretching such that the sinker loop and needle loop crests of the yarn thereof are substantially the only visible yarn portions in both the stretched and unstretched conditions of the article. The design is printed on the plain region whereby it maintains its definition and continuity of appearance when stretched in ordinary use.
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paper_title: Graph Cuts and Efficient N-D Image Segmentation

paper_content:

Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and level-sets. The segmentation energies optimized by graph cuts combine boundary regularization with region-based properties in the same fashion as Mumford-Shah style functionals. We present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea was widely studied in computer vision and graphics communities. We provide links to a large number of known extensions based on iterative parameter re-estimation and learning, multi-scale or hierarchical approaches, narrow bands, and other techniques for demanding photo, video, and medical applications.
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paper_title: HepaTux – A Semiautomatic Liver Segmentation System

paper_content:

While the MICCAI challenge strives for fully automatic liver segmentation, we present a fast semiautomatic method that is capable of segmenting livers with the help of an experienced human supervisor within 3 to 15 minutes. As fully automatic methods need to be checked for errors by a human observer anyway, we believe that for many practical applications the presented method will be useful.
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paper_title: Reconstruction and representation of 3D objects with radial basis functions

paper_content:

We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair incomplete meshes. An object's surface is defined implicitly as the zero set of an RBF fitted to the given surface data. Fast methods for fitting and evaluating RBFs allow us to model large data sets, consisting of millions of surface points, by a single RBF — previously an impossible task. A greedy algorithm in the fitting process reduces the number of RBF centers required to represent a surface and results in significant compression and further computational advantages. The energy-minimisation characterisation of polyharmonic splines result in a “smoothest” interpolant. This scale-independent characterisation is well-suited to reconstructing surfaces from non-uniformly sampled data. Holes are smoothly filled and surfaces smoothly extrapolated. We use a non-interpolating approximation when the data is noisy. The functional representation is in effect a solid model, which means that gradients and surface normals can be determined analytically. This helps generate uniform meshes and we show that the RBF representation has advantages for mesh simplification and remeshing applications. Results are presented for real-world rangefinder data.
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paper_title: Automatic 3D segmentation of the liver from abdominal CT images: a level-set approach

paper_content:

Computer-aided surgery requires the accurate registration of tomographic pre-operative images with intra-operative surface points acquired with 3D spatial localizers. Surface-based registration of these tomographic images with the surface points does, in turn, require a precise and accurate surface representation of the structures to be registered. This paper presents a level set technique for the automatic segmentation of the liver in abdominal CT scans. The main difficulty with level set methods is the design of appropriate speed functions for particular applications. Here we propose a novel speed function that is designed to (1) stop the propagating front at organ boundaries with weak edges, and (2) incorporate a-priori information on the relative position of the liver and other structures. We show that the new speed function we have designed to stop the front a weak edges is superior to other approaches proposed in the literature both on simulated images and on CT images. Results obtained with our approach for the segmentation of the liver in several CT scans are compared to contours obtained manually.
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paper_title: Two-stage Semi-automatic Organ Segmentation Framework using Radial Basis Functions and Level Sets

paper_content:

The automatic segmentation of complex anatomical struc- tures often fails due to low-contrast or missing edges, pathologic alter- ations, or high noise. As an alternative, we propose a novel two-stage semi-automatic algorithm that is able to segment complex structures like the liver shape with moderate user interaction. The first stage of our algorithm is the manual delineation of cross-sections of the anatom- ical structure in 2-D multi-planar reconstruction views. From this set of contours, an initial 3-D surface is reconstructed using radial basis func- tions. In a second step, the surface is evolved using a level set algorithm incorporating a new combination of both image information and shape information, the latter being derived from the initial contours. The al- gorithm has been evaluated for 10 Computed Tomography scans of the liver and has shown promising results.
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paper_title: Semi-automatic Segmentation of the Liver and its Evaluation on the MICCAI 2007 Grand Challenge Data Set

paper_content:

In this paper, we evaluate a semi-automatic liver segmentation method based on a level-set approach and a dynamically adapted speed func- tion. The approach also relies on a-priori anatomic information to reduce leak- age at the liver-rib interface. The numerical algorithms have been integrated into a complete system that permits loading DICOM images, segmenting these images, and visualizing the liver surface. Although the normal operation of the system assumes manual contour corrections, such corrections were not allowed for this evaluation. Results show that except for cases with large tumors that ex- tend to the liver surface, the method we propose is comparable to a human rater.
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paper_title: Nonrigid registration using free-form deformations: application to breast MR images

paper_content:

In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
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paper_title: Atlas based liver segmentation using nonrigid registration with a B-spline transformation model

paper_content:

Liver segmentation is an important step for the therapeutic decision making in liver surgery. However, manual segmentation is timeconsuming and tedious and so the need for accurate and robust automatic segmentation methods for clinical data arises. In this work an atlas in combination with nonrigid registration is used to segment the liver in actual clinical CT images. First, the atlas is built on twenty training images using nonrigid registration with a novel surface distance penalty. Next, this atlas is nonrigidly registered to ten test images. Currently, the user interaction is limited to the initialization of a rigid registration and to the definition of a region of interest for the nonrigid registration. Future work will focus on replacing the remaining user interaction with fully automatic procedures. Results are promising with an average overlap error of 10.4% and an average RMS distance of 5.0mm for the ten test images. Errors occur mainly at sites where the atlas is ill-defined such as the border between the heart and the liver.
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paper_title: Fully automatic anatomical , pathological , and functional segmentation from CT scans for hepatic surgery

paper_content:

To facilitate hepatic surgical planning, we have developed a new system for the automatic 3D delineation of anatomical and pathological hepatic structures from a spiral CT scan. This system also extracts functional information useful for surgery planning, such as portal vein labeling and anatomical segment delineation following the conventional Couinaud definition. From a 2 mm thick enhanced spiral CT scan, a first stage automatically delineates the skin, bones, lungs and kidneys, by combining the use of thresholding, mathematical morphological methods and distance maps. Next, a reference 3D model is immerged in the image and automatically deformed to the liver contour. Then an automatic Gaussians fitting on the imaging histogram allows to threshold the intensities of parenchyma, vessels and lesions. The next stage improves this first classification by an original topological and geometrical analysis, providing an automatic and precise delineation of lesions and veins. Finally, a topological and geometrical analysis based on medical knowledge provides the hepatic functional information invisible in medical imaging: portal vein labeling and hepatic anatomical segments. Clinical validation performed on more than 30 patients shows that this method allows a delineation of anatomical structures, often more sensitive and more specific than manual delineation by a radiologist.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
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paper_title: Segmentation of the liver using a 3D statistical shape model

paper_content:

This paper presents an automatic approach for segmentation of the liver from computer tomography (CT) images based on a 3D statistical shape model. Segmentation of the liver is an important prerequisite in liver surgery planning. One of the major challenges in building a 3D shape model from a training set of segmented instances of an object is the determination of the correspondence between different surfaces. We propose to use a geometric approach that is based on minimizing the distortion of the correspondence mapping between two different surfaces. For the adaption of the shape model to the image data a profile model based on the grey value appearance of the liver and its surrounding tissues in contrast enhanced CT data was developed. The robustness of this method results from a previous nonlinear diffusion filtering of the image data. Special focus is turned to the quantitative evaluation of the segmentation process. Several different error measures are discussed and implemented in a study involving more than 30 livers.
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paper_title: Global-to-Local Shape Matching for Liver Segmentation in CT Imaging

paper_content:

We propose a two-stage algorithm to segment the liver in CT images. First, we estimate the pose and global shape properties using a statistical shape model defined in the low dimensional space spanned by a training set of shapes. Then, we apply a template matching procedure to recover local deformations that were not present in the learning set. In both steps, we optimize the same image term: the likelihood of the intensity inside the region of interest and its background. The method requires a single seed point inside the liver for the initialization. We show that this global-to-local strategy is able to recover livers with peculiar shapes in arbitrary poses.
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paper_title: A shape-based approach to the segmentation of medical imagery using level sets

paper_content:

We propose a shape-based approach to curve evolution for the segmentation of medical images containing known object types. In particular, motivated by the work of Leventon, Grimson, and Faugeras, we derive a parametric model for an implicit representation of the segmenting curve by applying principal component analysis to a collection of signed distance representations of the training data. The parameters of this representation are then manipulated to minimize an objective function for segmentation. The resulting algorithm is able to handle multidimensional data, can deal with topological changes of the curve, is robust to noise and initial contour placements, and is computationally efficient. At the same time, it avoids the need for point correspondences during the training phase of the algorithm. We demonstrate this technique by applying it to two medical applications; two-dimensional segmentation of cardiac magnetic resonance imaging (MRI) and three-dimensional segmentation of prostate MRI.
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paper_title: Volumetric Medical Images Segmentation using Shape Constrained Deformable Models

paper_content:

In this paper we address the problem of extracting geometric models from low contrast volumetric images, given a template or reference shape of that model. We proceed by deforming a reference model in a volumetric image. This reference deformable model is represented as a simplex mesh submitted to regularizing shape constraint. Furthermore, we introduce an original approach that combines the deformable model framework with the elastic registration (based on iterative closest point algorithm) method. This new method increases the robustness of segmentation while allowing very complex deformation of the original template. Examples of segmentation of the liver and brain ventricles are provided.
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paper_title: Efficient and reliable schemes for nonlinear diffusion filtering

paper_content:

Nonlinear diffusion filtering in image processing is usually performed with explicit schemes. They are only stable for very small time steps, which leads to poor efficiency and limits their practical use. Based on a discrete nonlinear diffusion scale-space framework we present semi-implicit schemes which are stable for all time steps. These novel schemes use an additive operator splitting (AOS), which guarantees equal treatment of all coordinate axes. They can be implemented easily in arbitrary dimensions, have good rotational invariance and reveal a computational complexity and memory requirement which is linear in the number of pixels. Examples demonstrate that, under typical accuracy requirements, AOS schemes are at least ten times more efficient than the widely used explicit schemes.
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paper_title: Efficient Kernel Density Estimation of Shape and Intensity Priors for Level Set Segmentation

paper_content:

We propose a nonlinear statistical shape model for level set segmentation which can be efficiently implemented. Given a set of training shapes, we perform a kernel density estimation in the low dimensional subspace spanned by the training shapes. In this way, we are able to combine an accurate model of the statistical shape distribution with efficient optimization in a finite-dimensional subspace. In a Bayesian inference framework, we integrate the nonlinear shape model with a nonparametric intensity model and a set of pose parameters which are estimated in a more direct data-driven manner than in previously proposed level set methods. Quantitative results show superior performance (regarding runtime and segmentation accuracy) of the proposed nonparametric shape prior over existing approaches.
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paper_title: Use of active shape models for locating structures in medical images

paper_content:

Abstract This paper describes a technique for building compact models of the shape and appearance of flexible objects (such as organs) seen in 2D images. The models are derived from the statistics of labelled images containing examples of the objects. Each model consists of a flexible shape template, describing how the relevant locations of important points on the objects can vary, and a statistical model of the expected grey levels in a region around each model point. We describe how the models can be used in local image search, and give examples of their application to medical images.
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paper_title: Nonrigid registration using free-form deformations: application to breast MR images

paper_content:

In this paper the authors present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, the authors have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.
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paper_title: Nearest Neighbor Pattern Classification

paper_content:

The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
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paper_title: Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching

paper_content:

A fully automatic system for segmentation of the liver from CT scans is presented. The core of the method consists of a voxel labeling procedure where the probability that each voxel is part of the liver is estimated using a statistical classifier (k-nearest-neighbor) and a set of features. Several features encode positional information, obtained from a multi-atlas registration procedure. In addition, pre-processing steps are carried out to determine the vertical scan range of the liver and to rotate the scan so that the subject is in supine position, and post-processing is applied to the voxel classification result to smooth and improve the final segmentation. The method is evaluated on 10 test scans and performs robustly, as the volumetric overlap error is 12.5% on average and 15.3% for the worst case. A careful inspection of the results reveals, however, that locally many errors are made and the localization of the border is often not precise. The causes and possible solutions for these failures are briefly discussed.
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paper_title: PET-CT image registration in the chest using free-form deformations

paper_content:

We have implemented and validated an algorithm for three-dimensional positron emission tomography transmission-to-computed tomography registration in the chest, using mutual information as a similarity criterion. Inherent differences in the two imaging protocols produce significant nonrigid motion between the two acquisitions. A rigid body deformation combined with localized cubic B-splines is used to capture this motion. The deformation is defined on a regular grid and is parameterized by potentially several thousand coefficients. Together with a spline-based continuous representation of images and Parzen histogram estimates, our deformation model allows closed-form expressions for the criterion and its gradient. A limited-memory quasi-Newton optimization algorithm is used in a hierarchical multiresolution framework to automatically align the images. To characterize the performance of the method, 27 scans from patients involved in routine lung cancer staging were used in a validation study. The registrations were assessed visually by two expert observers in specific anatomic locations using a split window validation technique. The visually reported errors are in the 0- to 6-mm range and the average computation time is 100 min on a moderate-performance workstation.
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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: Liver segmentation by intensity analysis and anatomical information in multi-slice CT images

paper_content:

Purpose ::: Quantitative assessment and essentially segmentation of liver and its tumours are of clinical importance in various procedures such as diagnosis, treatment planning, and monitoring. Moreover, segmentation of liver is the basis of further processing such as visualization, liver resection planning, and liver shape analysis. In this paper, we propose an algorithm to estimate an initial liver boundary.
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paper_title: Segmentation of the liver using the deformable contour method on CT images

paper_content:

Automatic liver segmentation from abdominal computed tomography (CT) images is one of the most important steps for computer-aided diagnosis (CAD) for liver CT. However, the liver must be separated manually or semi-automatically since surface features of the liver and partial-volume effects make automatic discrimination from other adjacent organs or tissues very difficult. In this paper, we present an unsupervised liver segmentation algorithm with three steps. In the preprocessing, we simplify the input CT image by estimating the liver position using a prior knowledge about the location of the liver and by performing multilevel threshold on the estimated liver position. The proposed scheme utilizes the multiscale morphological filter recursively with region-labeling and clustering to detect the search range for deformable contouring. Most of the liver contours are positioned within the search range. In order to perform an accurate segmentation, we produce the gradient-label map, which represents the gradient magnitude in the search range. The proposed algorithm performed deformable contouring on the gradient-label map by using regular patterns of the liver boundary. Experimental results are comparable to those of manual tracing by radiological doctors and shown to be efficient.
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paper_title: Liver segmentation from computed tomography scans: A survey and a new algorithm

paper_content:

Objective: In the recent years liver segmentation from computed tomography scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver disease diagnosis, liver volume measurement, and 3D liver volume rendering. ::: ::: Methods: In this paper we report a review study about the semi-automatic and automatic liver segmentation techniques, and we describe our fully automatized method. ::: ::: Results: The survey reveals that automatic liver segmentation is still an open problem since various weaknesses and drawbacks of the proposed works must still be addressed. Our gray-level based liver segmentation method has been developed to tackle all these problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra- and inter-observer variation. ::: ::: Conclusions: We believe that our technique outperforms those presented in the literature; nevertheless, a common test set with its gold standard traced by experts, and a generally accepted performance measure are required to demonstrate it.
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paper_title: Improved automatic liver segmentation of a contrast enhanced CT image

paper_content:

This paper presents an improved automatic liver segmentation method using a left partial histogram threshold (LPHT) algorithm. The LPHT algorithm removes other neighboring abdominal organs regardless of pixel variation of contrast enhanced computed tomography (CE-CT) images. After histogram transformation, adaptive multi-modal threshold is used to find the range of gray-level values of the liver structure. The LPHT algorithm is performed to removing other neighboring organs. Then, binary morphological filtering is processed to remove unnecessary objects and smooth the boundary. 48 CE-CT slices of twelve patients were selected to test the proposed automatic liver segmentation. As evaluation methods, normalized average area and area error rate were used. The results of experiments show similar performance between the proposed algorithm and the manual method by a medical doctor.
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paper_title: Liver extraction in the abdominal CT image by watershed segmentation algorithm

paper_content:

In this paper, we proposed a liver extracting procedure for computer aided liver diagnosis system. An extraction of liver region is difficult due to interferences of other organs in the abdominal CT image. For this reason, liver region is extracted in ROI. ROI is selected by using the window for measuring the distribution of liver region’s Hounsfield Unit in abdominal CT image. The distribution is measured by using an existential probability of liver’s value in the window. If the probability of any window exceeded 50%, it would be assigned to ROI. Actually, liver region is not clearly discerned from the adjacent organs as muscle, spleen, and pancreas in abdominal CT image. Liver region is extracted by the watershed segmentation algorithm which is effective in this situation. This segmentation algorithm is very sensitive to the slight variance of contrast. So it generally produces over-segmentation regions. For optimal segmentation these regions are required to merge into the significant regions. Finally, a liver region is selected and extracted by prior information based on anatomic information.
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paper_title: Abdominal organ segmentation using texture transforms and a Hopfield neural network

paper_content:

Abdominal organ segmentation is highly desirable but difficult, due to large differences between patients and to overlapping grey-scale values of the various tissue types. The first step in automating this process is to cluster together the pixels within each organ or tissue type. The authors propose to form images based on second-order statistical texture transforms (Haralick transforms) of a CT or MRI scan. The original scan plus the suite of texture transforms are then input into a Hopfield neural network (HNN). The network is constructed to solve an optimization problem, where the best solution is the minima of a Lyapunov energy function. On a sample abdominal CT scan, this process successfully clustered 79-100% of the pixels of seven abdominal organs. It is envisioned that this is the first step to automate segmentation. Active contouring (e.g., SNAKE's) or a back-propagation neural network can then be used to assign names to the clusters and fill in the incorrectly clustered pixels.
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paper_title: Liver segmentation for CT images using GVF snake

paper_content:

Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.
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paper_title: Identifying multiple abdominal organs from CT image series using a multimodule contextual neural network and spatial fuzzy rules

paper_content:

Identifying abdominal organs is one of the essential steps in visualizing organ structure to assist in teaching, clinical training, diagnosis, and medical image retrieval. However, due to partial volume effects, gray-level similarities of adjacent organs, contrast media affect, and the relatively high variations of organ position and shape, automatically identifying abdominal organs has always been a high challenging task. To conquer these difficulties, this paper proposes combining a multimodule contextual neural network and spatial fuzzy rules and fuzzy descriptors for automatically identifying abdominal organs from a series of CT image slices. The multimodule contextual neural network segments each image slice through a divide-and-conquer concept, embedded within multiple neural network modules, where the results obtained from each module are forwarded to other modules for integration, in which contextual constraints are enforced. With this approach, the difficulties arising from partial volume effects, gray-level similarities of adjacent organs, and contrast media affect can be reduced to the extreme. To address the issue of high variations in organ position and shape, spatial fuzzy rules and fuzzy descriptors are adopted, along with a contour modification scheme implementing consecutive organ region overlap constraints. This approach has been tested on 40 sets of abdominal CT images, where each set consists of about 40 image slices. We have found that 99% of the organ regions in the test images are correctly identified as its belonging organs, implying the high promise of the proposed method.
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paper_title: Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

paper_content:

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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paper_title: Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets

paper_content:

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
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<format>
Title: Survey on Liver CT Image Segmentation Methods

Section 1: Introduction
Description 1: This section introduces the challenges and complexity of liver CT image segmentation, highlighting key factors that make accurate segmentation difficult.

Section 2: Liver Volume Segmentation
Description 2: This section categorizes liver segmentation methods into semi-automatic and automatic techniques and provides an overview of each category.

Section 3: Semi-automatic Liver Segmentation Methods
Description 3: This section details various semi-automatic methods used for liver segmentation, including graph-based, region-growing based, level sets based, and atlas matching methods.

Section 4: Graph-based Semi-automatic Methods
Description 4: This section explains how graph-based methods utilize graph structures and user intervention to segment liver images.

Section 5: Region-growing Based Semi-automatic Methods
Description 5: This section discusses methods that apply region-growing algorithms, requiring user intervention to correct segmentation inaccuracies.

Section 6: Level Sets Based Semi-automatic Methods
Description 6: This section describes the level sets method for segmentation and how it uses user-defined contours to segment liver in CT images.

Section 7: Atlas Matching Semi-automatic Methods
Description 7: This section covers probabilistic atlases and how they are generated and utilized for liver segmentation with semi-automatic techniques.

Section 8: Fully-automatic Liver Segmentation Methods
Description 8: This section introduces various fully automated techniques for liver segmentation and discusses their advantages and limitations.

Section 9: Statistical Shape Model Based Automatic Methods
Description 9: This section discusses fully automatic methods that use statistical shape models built from training data to segment the liver.

Section 10: Probability Atlas Based Automatic Methods
Description 10: This section explores methods that use voxel classification and multi-atlas registration to achieve fully automatic liver segmentation.

Section 11: Region-growing Based Automatic Methods
Description 11: This section describes automatic region-growing techniques that require no user intervention for segmenting liver CT images.

Section 12: Rule Based Automatic Methods
Description 12: This section details rule-based methods that use predefined rules to segment various anatomical structures, including the liver.

Section 13: Gray-level Based Automatic Methods
Description 13: This section discusses methods relying on statistical analysis and histogram transformations to segment the liver automatically.

Section 14: Evaluation Measurements
Description 14: This section outlines how liver segmentation methods are evaluated, comparing automated segmentation results with manual ones, and lists common evaluation metrics.

Section 15: Comparative Study for Liver Segmentation Methods
Description 15: This section compares the strengths and weaknesses of various liver segmentation methods and emphasizes the challenges in obtaining a common evaluation standard.

Section 16: Conclusions
Description 16: This section summarizes the findings of the survey, emphasizing open challenges and potential directions for future research in liver CT image segmentation.

</format>
