## 9 Results and Discussion  
Two expert radiologists were asked to evaluate the performance of the proposed fat segmentation and classification method and the liver segmenting method. They reported that in one subject, the liver was segmented with a major error. The reason for this observation is that the proposed method failed to segment an abnormal lesion in the left lobe for that subject. Regardless of this case, the livers in all the other subjects were ranked as well segmented with minor error. According to the ability of the method to distinguish liver tissues from non-liver tissues, the radiologists reported that the method shows good capability to implement the task. However, the radiologists noticed that in some rare cases, small parts of the gallbladder and bowel were segmented as a liver. They ranked this error as a minor error. Additionally, they reported that the total fat segmentation results in all of the employed CT images were acceptable. The separation of SF and VF was accepted in almost the entire employed image. Only 0.174% of the employed images failed to detect a correct boundary for the abdomen wall. In these cases, parts of the SV were classified as VF, and in some other cases, the VF was classified as SF. In total, 300 CT images were employed to evaluate the proposed fat segmentation classification method objectively. These CT images were manually segmented by expert radiology. These 300 CT slices were selected from 30 subjects (10 CT images/subject). The selected CT images were chosen between the first and fifth lumbar vertebrae (L1-L5). The manually segmented CT images were compared to those produced by the proposed fat  
Figure 8: Structure of the abdomen fat segmentation method. Figure 9: SF and VF classification operation. segmentation classification method. Subjectively, the proposed fat segmentation classification method was also evaluated. For each subject, the fat tissues in the entire CT images between L1 and L5 were segmented and classified by the proposed method, and the results were evaluated by two radiologists. Experimental results show that the performances of the abdominal fat segmentation method and the liver segmentation method are very promising. The abdominal fat segmentation method shows a strong capability to handle a wide variety of abdominal wall shapes. The liver segmentation method also shows good performance. Several challenges and difficulties due to the similarity of gray level intensities of the liver and the attached organs have been overcome in the proposed liver segmentation method. The average of the accuracy and the mean error rate for the total fat, SF, and VF for the used data set (300 CT images) were used to evaluate the performance of the fat segmentation and classification method. The fat-segmented and classified data collected from the two expert radiologists were used to classify the fat pixels segmented and classified by the study method. The accuracy was defined as the percentage of correctly classified pixels (fat and non-fat) with respect to the total number of pixels in the images, whereas the mean error rate was defined as the percentage of incorrectly classified pixels (fat and non-fat) with respect to the total number of pixels in the images. The average accuracies for the three types of fat (total fat, subcutaneous fat, and visceral fat) were found to be in the range of 98.52 % to 99.95%, and mean error rate is found to be in the range of 0.4% to 1.48%. Figure 10 shows some CT sample images and the results of fat segmentation and classification. The top row images are the original DICOM images, and the bottom row presents the results of fat segmentation and classification for the top DICOM images. The red color region represents the VF tissue, and the turquoise color region represents the SF tissues. The liver segmentation method was evaluated subjectively by the same two radiologists. For each subject, the radiologists were asked to evaluate the results of the segmented liver in each CT data set based on his/her observation.