Neural Network for Classification of Focal Liver Lesions in Ultrasound Images

Abstract

A novel method of multiscale texture analysis based on neural networks (NNs) has been developed and applied to the automated classification of benign and malignant focal liver lesions in ultrasound images. Our method is unique in the sense that it integrates a process of selection of multiscale texture features and a process of classification by a NN for effective classification. We developed an automated method that selects a set of multiscale texture features in the wavelet domain which maximize the performance of the NN for a given classification task. For the automated classification of benign and malignant focal liver lesions, regions of interest (ROIs) extracted from within the lesions were decomposed into subimages by wavelet packets. Multiscale texture features that quantify the homogeneity of the echogenicity were calculated from these subimages and were combined by a NN. A subset of the multiscale features was selected that yielded the highest performance in the classification of lesions. In an analysis of a set of ROIs extracted from hemangiomas (benign lesions), and from hepatocellular carcinomas and metastases (malignant lesions), the multiscale features yielded a high performance in distinguishing the benign from the malignant lesions. Therefore, our new multiscale texture analysis method based on NNs has the promise of increasing the accuracy of diagnosis of focal liver lesions in ultrasound images.