Evaluation of feature extraction methods for classification of liver abnormalities in ultrasound images

Abstract

Image analysis techniques have played an important role in several medical applications. In this paper, the classification of ultrasonic liver images is studied by using texture features extracted from Laws' method, Autocorrelation method, Edge frequency methods, Gabor Wavelet method and Co-occurrence probability method. Then the best features from different methods are combined to improve the classification. The features from these methods are used to classify four sets of ultrasonic liver images – Normal, Cyst, Benign and Malignant, and how well they suit in classifying the abnormalities is reported. A Neural Network classifier is employed to evaluate the performance of these features based on their recognition ability.