Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation

Preliminary diagnosis based on ultrasound scanning is the first step in the treatment of many abdominal diseases. The noisy nature of the ultrasound image coupled with minimal contrasting features complicates the task of automatic classification if not impossible. This study presents a segmentation-based approach to automatic classification of ten types of diffused and focal liver diseases from ultrasound images. A novel approach using Isocontour Segmentation based on Marching Squares, a computer graphics algorithm is presented. GLCM and fractal features are extracted from the segmented ultrasound images and classified using support vector machines and artificial neural networks (ANN) and the results are analysed. An overall classification accuracy of 92% is achieved using fractal features and ANN.