Liver segmentation with new supervised method to create initial curve for active contour

The liver performs a critical task in the human body; therefore, detecting liver diseases and preparing a robust plan for treating them are both crucial. Liver diseases kill nearly 25,000 Americans every year. A variety of image segmentation methods are available to determine the liver's position and to detect possible liver tumors. Among these is the Active Contour Model (ACM), a framework which has proven very sensitive to initial contour delineation and control parameters. In the proposed method based on image energy, we attempted to obtain an initial segmentation close to the liver's boundary, and then implemented an ACM to improve the initial segmentation. The ACM used in this work incorporates gradient vector flow (GVF) and balloon energy in order to overcome ACM limitations, such as local minima entrapment and initial contour dependency. Additionally, in order to adjust active contour control parameters, we applied a genetic algorithm to produce a proper parameter set close to the optimal solution. The pre-processing method has a better ability to segment the liver tissue during a short time with respect to other mentioned methods in this paper. The proposed method was performed using Sliver CT image datasets. The results show high accuracy, precision, sensitivity, specificity and low overlap error, MSD and runtime with few ACM iterations.