AUTOMATIC LIVER SEGMENTATION METHOD USING NON-CONTRAST ENHANCED CT IMAGES FOR LIVER FAT EVALUATION BY MATLAB

Evaluating the diffused fat in the liver requires an accurate segmentation for the liver tissues. Miss segmenting the non-liver organs or tissues may lead to a negative impact on the credibility of the obtained results. The segmenting of liver has been proposed as adaptive method by using non-contrast enhanced CT images (NCT). In this method, minimizing the error of segmenting non-liver tissues is the main objective. The proposed method is improved the robustness without utilized training data in building our model or in calculating all parameters. A fully automatic liver segmentation method is suggested in this paper. In this method, the liver of a subject is segmented using NCT data slice-by-slice. The method of segmentation is based on using thresholding operation, gray-level information, Gaussian gradient transformation, region growing algorithm, distance transformation, edge detection and anatomy information. Data sets of 30 subjects are employed to evaluate the proposed method subjectively. Results show a great capability to separate attached organs from the liver. The ability of the method to segment the liver tissues did not reach to a great level. However, the results of segmented liver can be considered as accepted results for the main objective of this study. The method shows a feasible capability to separate non-liver organs and tissues. The results indicate that chances for mistakenly segmentation for nonliver tissues as liver are very low.