Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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. <s> BIB001 </s> Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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. <s> BIB002 </s> Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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. <s> BIB003 </s> Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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%. <s> BIB004 </s> Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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. <s> BIB005 </s> Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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. <s> BIB006 </s> Survey on liver CT image segmentation methods <s> Gray-level based automatic methods <s> 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. <s> BIB007
Methods under this category are based on a statistical analysis for CT slices that are segmented manually to estimate the liver gray levels. Some other methods use the histogram analysis based on a prior information about the liver intensity range for the estimating the liver gray levels. These estimated values are employed with a simple or repetitive process of thresholding to produce a binary map which represents the liver. Next, this binary image is processed morphologically to eliminate attached organs. The retrieved information from the previous segmented image is used as a base for segmenting the current image. Finally, the boundary of each CT image is smoothed by using active contours or B splines. The authors in  BIB003  proposed methods for automatic liver segmentation based on the idea mentioned above; the researchers study the distribution of the liver intensity form a manually segmented CT samples. The initial liver volume is extracted by exploiting prior information about the location of the liver and information extracted from the analysis of the manually segmented CT samples. Next, K-mean algorithm and morphological filtering are used get rid of organs attached to the liver, and to determine the final liver boundary. Finally, active contours are applied to smooth the boundary of the liver based on the gradient direction, the intensity distribution, and the pattern features of the liver. Tsai and Tanahashi (1994) employ a feed forward neural network trained with back propagation on liver, liver boundary and non-liver sub-images, while BIB001 used an unsupervised Hopfield neural network, the images and a set of Haralick's texture features are the inputs. Both methods are tested by using only one image and they obtained a very low performance. BIB002 obtained promising results when they used an unsupervised contextual neural network procedure based on pixel gray levels and spatial information to recognize and detached different organs. Seven fuzzy rules are employed based on the position, size and shape of each organ. Organs boundaries are refined based on the relationships among the organs shapes in subsequent images. Their method suffers from some problems. First, when two neighbor organs share close gray levels, the chances for the system to fail increased. Second, the method is not robust to the variations of databases duo to employing of fuzzy rules that experimentally set the threshold values. BIB004 identify the liver peak in the histogram of the whole volume, and determined two threshold values at each side of the peak. A liver binary volume is created by using the threshold values, after that, attached neighbor organs are deleted by a set morphological operations. The Canny edge detector and the 3D liver binary volume are used to extract the external boundary of the liver. Edges are selected and used as an input to the gradient vector flow algorithm to fine the initial liver segmented 3D volume. In the final step, the boundary of the liver will be refined, this operation requires a manual selection for the starting CT image where the result of liver segmentation is accurate and the liver has a large profile area. The contour of the liver in the current slice is used as a mask for the adjacent slices. An iterative correction process that proceeds in a slice-by-slice manner is employed, and the correct segmentation result from the previous slice is used to detect and eliminate errors in the current slices. BIB005 present an automatic method for segmenting liver in contrast enhanced CT images based on a left partial histogram threshold (LPHT) algorithm. This algorithm eliminates neighboring organs regardless of the pixels variations. The histogram transformation is followed by an adaptive multi-modal threshold that finds the gray-level ranges of the liver. Finally, morphological filtering is processed to smooth the boundary and eliminate unnecessary objects. BIB006 proposed a method for extracting liver in CT images and employed it in computer aided liver diagnosis system. The liver distribution is measured and used to select the region of interesting (ROI). By using an existential probability of liver's value in a window, the window will be assigned as ROI when the probability passed 50%. Then, the watershed segmentation algorithm is employed to extract the liver regions. For optimal segmentation, segmented regions are merged into the significant regions. Finally, a liver region is selected by priory knowledge about the anatomic information of the liver. Fully automatic liver segmentation method has been developed by BIB007 . They described their method as a low computational cost gray-level based technique where no data samples are needed. Furthermore, all employed parameters are not crucial. Some parameters are based on anatomical information, while the rest of the parameters have been guided by reasoning. The method implementation starts with the heart volume segmentation based on a priory anatomical knowledge. Then, five labeled partitions of the abdominal volume are selected. These partitions are used to calculate a set of parameters to create the edge map to detect the image details. All the information and parameters are employed with the region growing algorithm to segment the abdomen organs based on the assumption that the gray levels for each organ follow a Gaussian distribution. A method for liver segmentation consists of four steps is proposed by Foruzan et al. (2009) . In the first step, a large cross-section of liver CT slice is selected and used to calculate statistical parameters for liver's intensity, utilizing expectation maximization (EM) algorithm. Secondly, ROI is defined by the extraditing of the ribs and the heart. In the third step, a double thresholding method is proposed where the liver intensity range is divided into two overlapping ranges, a decision table is employed to label objects as a liver candidates or disregard them. Finally, an anatomical based rule is used to finalize the candidates as a liver tissue.