## 8 Segmenting the Liver in Iss
The same process explained for _Iss_ was repeated for each CT slice with some modifications. The liver segmentation process for each CT slice can be summarized as follows:  
**Step 1:** The previous segmented liver is eroded using an adaptive structural element. The size of the structural element depends on the size of the segmented liver. **Step 2:** The projection of the eroded region on the current CT slice is used to find new parameters. The current CT slice is thresholded using the new parameters. The eroded region will also be used as the seed region on the region growing algorithm. **Step 3:** Skin, abdomen wall muscles, and the right bottom quarter of the current CT slice is removed, and the region growing algorithm is applied to the current CT slice as in Iss. **Step 4:** Elementary logic and operation between the resulting binary image from applying the region growing algorithm and the segmented liver in the previous CT slice are applied. **Step 5:** Finally, the liver extension method is applied to the resulting image  
Figure 5: Liver segmentation enhancement method demonstration. (**a**) Original case, (**b**) result of the conventional dilation, (**c**) result of the proposed dilation. The operation in Step 4 is very important in case an attached organ started to appear, and the region growing algorithm did not stop at the actual boundary of the liver. In these cases, the growth of the liver region passed the liver to the attached organ. Because the size of the liver region in subsequent CT slices was almost the same, applying the elementary logic and operation between the segmented liver and the region growing result image eliminated the attached region from the segmented liver. Applying the liver extension method on the current processed CT slice after this helped to improve the final segmented liver in the current slice. Segmenting the liver slice by slice was continued in this way until the size of the segmented liver reached a specified small size. The human liver has two large sections, called the right and the left lobes. These two lobes usually appeared in some CT slices as two separate regions. In such cases, the proposed method handled each region separately, and the final results for both regions were combined. The first point in the modification list states that the segmented liver from the previous CT slice was eroded. If the liver in this CT slice is presented in two regions, there is a strong chance that the liver region with the smaller size was removed because of the erosion operation. Therefore, each region was eroded separately using a structural element where its size depended on the size of the region. In the proposed method, Iss subsequent CT slices were tested individually (one by one) to check whether the liver in the previous CT slice was segmented as two regions or one region. The automatic abdomen fat segmentation method used in this study is a robust method that is employed to detect and classify the abdominal fat in non-contrast-enhanced CT images. In this method, a modified fuzzy c-means (FCM) [25; 26] clustering technique and  
Figure 6: Final liver segmentation in the starting slices. the Otsu thresholding technique [27] were used to segment fat tissues. A non-fat binary image was used to detect and trace the boundary of the abdominal wall. This boundary was used to separate SF from VF. The process of detecting this boundary started with finding the boundaries of regions in a non-fat binary image (after removing the bed and any artificial elements that may appear). The non-fat binary image was scanned top to bottom, bottom to top, left to right, and right to left. During the scanning operation, whenever a foreground pixel was detected, the scanning in that line was terminated, and the detected pixel was considered as an abdomen wall boundary pixel. Every single pixel in this boundary image must be connected to two other pixels; if a pixel was detected with one or no connected pixels, it was connected to the nearest one or two boundary pixels.