Detection of liver tumor in CT images using watershed and hidden markov random field expectation maximization algorithm

Abstract

Precisely segmenting liver from computed tomography (CT) scan images is a challenging task of computer aided diagnosis. The first and crucial step for diagnosis is automatic liver segmentation. In this paper, the watershed transform, Hidden Markov Random Field- Expectation Maximization (HMRF-EM) and threshold algorithms have been used for visualizing and measuring the tumor area which is a part of liver which is segmented from CT abdominal images. The proposed process was tested on a series of CT scan images of liver. The segmentation and area estimation images are obtained by the study of 2D images. To validate the proposed approach tumor area, MSE and PSNR values are measured from the segmented region which helps the physician for a successful treatment and diagnosis procedure.