## 2 Liver Segmentation and Abdominal Fat Segmentation/Classification Methods  
Methods presented in the literature to obtain liver segmentation were reviewed in detail by [PERSON] et al. [19] ([PERSON] is the formal last name of [PERSON], who is one of the authors of this research). In this review, liver segmentation methods are divided into two categories: semi- and fully automatic methods. For each category, several methods, approaches, related issues, and problems were defined and discussed. [PERSON] at. al. [19] found that gray-level-based techniques obtain the most promising results among the reviewed methods, but they are not robust to database variations. Gray level estimation does not consider the high variability of CT intensity values. Therefore, the performance could decrease significantly when larger and more complex data sets are used. Additionally, most methods require manual intervention and for some critical parameters to be experimentally estimated. It was also found that learning techniques are strongly dependent on the training set. Model-based techniques and probabilistic atlases suffer from the same difficulty: they need a lot of data to be properly collected and manually segmented to produce the model. In addition, processing non-standard liver shapes increase the chances for the algorithms to fail. The fat tissue normal CT attenuation is defined based on Hounsfield units (Hu) as the interval within (\(-190<\text{Hu}<-30\)) [19; 20; 21]; the average of the fat tissue attenuation varies across subjects, and it depends on the CT scanner used [20]. [PERSON] et al. [20] proposed a method to estimate the abdominal fat distribution using a threshold within the mean \(\pm\) two standard deviations. This method did not consider the spatial relationship of the pixels. Their method included some user interaction to calculate the mean and the standard deviation to separate the SF from the VF. [PERSON] et al. [21] included a semi-automatic method to separate the two types of fat from each other. Two binary masks were created, one for the skin of the subject and the other for the abdomen wall. Pixels between these two masks were classified as SF, and pixels inside the mask of the abdomen wall were classified as VF. However, the classification of the fat tissues was based on a fixed interval of intensities, and user interaction is required to remove any artifact that may appear in the CT image. The analysis of histograms was introduced by [PERSON] et al. [22] in their human structure recognition system, where the CT images were divided into four parts (air, fat, organs and muscles, and bones) based on the distribution of these tissue intensities. A dynamic process was applied to estimate the gray-level threshold values to implement the segmentation operation. To separate the SF from the VF, the distances between the skin pixels and muscles/organs were calculated using the Euclidean distance transformation algorithm. The measured distances were used to extract the SF from the fat binary image. The fat regions, except the extracted SF, were considered as the VF. [PERSON] et al. [23] presented an automatic abdomen fat tissue segmentation method that combined intensity and texture information; this combination was then used as an input to the FCM clustering technique to implement the segmentation task. They found that the combination of intensity and spot-spot texture feature (derived from Laws' features) provided the highest discrimination between fat and non-fat tissues. Statistics of fat tissues were calculated by automatic selection of several seed points using an active shape model, and a template was constructed to separate the SF from the VF tissues. Considering the texture as a feature in the fat segmentation methods requires a process to find the best texture features that describe the fat tissues. Therefore, training data must be collected and labeled manually, and then the combination of the most discriminating features must be selected.