Description

ABSTRACT

Ultrasound imaging is considered to be one of the most cost-effective and non-invasive techniques for conclusive diagnosis in some cases and preliminary diagnosis in others. Automatic liver tissue characterization and classification from ultrasonic scans have been for long, the concern of many researchers, and has been made possible today by the availability of the most powerful and cost effective computing facilities. Ultrasound image intensity and textural features are jointly used with clinical and laboratorial data in the staging process. In this paper we propose a Neuro -fuzzy classifier to classify the liver is normal or fatty liver. The features that have been extracted are based on moments, image partition, principal component axes (PCA), correlation coefficient and perturbed moments. In image partition method, the image is divided into four parts with three different ways. The principal component axes (PCA) method has been used to balance the distribution of pixels in different regions of the image. Correlation coefficient provides dependencies of different moments on each other. In perturbed moment method, moment invariants are computed by small perturbation in image and information is extracted from the perturbation. Meanwhile, the multi-resolution fractal feature vector provides good discrimination ability to classify the three types of ultrasonic liver images under study.