An Improved Method for Liver Diseases Detection by Ultrasound Image Analysis

Ultrasound imaging is a popular and noninvasive tool frequently used in the diagnoses of liver diseases. A system to characterize normal,fatty and heterogeneous liver, using textural analysis of liver Ultrasound images, is proposed in this paper. The proposed approach isable to select the optimum regions of interest of the liver images. These optimum regions of interests are analyzed by two level waveletpacket transform to extract some statistical features, namely, median, standard deviation, and interquartile range. Discriminationbetween heterogeneous, fatty and normal livers is performed in a hierarchical approach in the classification stage. This stage, first,classifies focal and diffused livers and then distinguishes between fatty and normal ones. Support vector machine and k‑nearestneighbor classifiers have been used to classify the images into three groups, and their performance is compared. The Support vectormachine classifier outperformed the compared classifier, attaining an overall accuracy of 97.9%, with a sensitivity of 100%, 100%and 95.1% for the heterogeneous, fatty and normal class, respectively. The Acc obtained by the proposed computer‑aided diagnosticsystem is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists andexperts in liver diseases interpretation.