## 3 Materials and Methods  
This prospective study included a total of 125 patients. The data set of 35 subjects (20 male subjects, 36 to 62 years old, mean age 49 years; 15 female subjects, 37 to 72 years old, mean age 51 years) was acquired using SIEMENS scanner at Country Heights Health Tourism Sdn. Bhd. Scanning was performed at 130 kV and 200 mA. The resolutions of the slices were 512 \(\times\) 512 pixels, and the slice thickness was 2.5 mm in all subjects. The data set of the remaining subjects (57 male subjects, 24 to 72 years old, mean age 46 years; 33 female subjects, 26 to 70 years old, mean age 50 years) was acquired using a TOSHIBA scanner at the hospital of Serdang. Scanning was performed at 120 kV and 200 mA. The resolutions of the slices were 512 \(\times\) 512 pixels, and the slice thickness was 2 mm or 3 mm. Both health centers are in the state of Selangor, Malaysia. The data set used is not publicly available due to the rights of Country Heights Health Tourism Sdn. Bhd and the hospital of Serdang located in Malaysia. Figure 1 shows some CT image samples from the employed data set.  
For each subject in this investigation, the data set was processed through three main stages. In the first stage, an automated method of segmenting and classifying abdomen fat was implemented using CT images. The segmented fat tissues in the abdominal region of the subject were classified as SF and VF. In the second stage, an automated method of liver segmentation method using CT images was implemented, and the segmented liver was employed to evaluate how fatty the liver was. In the last stage, the results obtained from stage one and stage two were analyzed to evaluate the correlation between the level of diffused fat in the liver and obesity. The employed images in this research are gray-scale non-contrast-enhanced CT images stored in DICOM format. Figure 2 shows a block diagram of the methodology.  
A fully automatic liver segmentation method using non-contrast-enhanced CT images was used in this research. In this method, the liver is segmented into slices one by one. Gray level, Gaussian gradients, region growing algorithm, distance transformation, canny edge detector, and anatomic information play the main roles in the liver segmentation method. The details of the liver segmentation method used in this research have been published by one of the authors of this research [24]. The first slice (Iss) was selected automatically from the data set of a subject where the liver covers a wide area. The segmented liver in Iss is used as a reference for the adjacent slices. The same employed techniques to segment the liver in the starting slice are employed to segment the liver in all other CT slices of the subject. Whenever the liver in a new slice is segmented, it will be used as a reference for the next slice. The procedure will keep going in this manner until it reaches the top of the liver and the liver's bottom lobe. Figure 3 shows the structure of the proposed method.  
The procedure of segmenting the liver in the starting slice is presented in the following steps:  
1. Finding the slice where the liver starts to appear.
2. Finding the liver seed region.
3. Calculating the liver parameters.
4. Selecting the starting CT Slice (Iss).
5. Segmenting the liver in Iss.  
Figure 1: CT image samples.