Neural network based focal liver lesion diagnosis using ultrasound images.

Present study proposes a computer-aided diagnostic system to assist radiologists in identifying focal liver lesions in B-mode ultrasound images. The proposed system can be used to discriminate focal liver diseases such as Cyst, Hemangioma, Hepatocellular carcinoma and Metastases, along with Normal liver. The study is performed with 111 real ultrasound images comprising of 65 typical and 46 atypical images, which were taken from 88 subjects. These images are first enhanced and then regions of interest are segmented into 800 non-overlapping segmented regions-of-interest. Subsequently 208-texture based features are extracted from each segmented region-of-interest. A two step neural network classifier is designed for classification of five liver image categories. In the first step, a neural network classifier gives classification among five liver image categories. If neural network decision is for more than one class as obtained from the first step, binary neural network classifiers are used in the second step for crisp classification between two classes. Test results of two-step neural network classifier showed correct decisions of 432 out of 500 segmented regions-of-interest in test set with classification accuracy of 86.4%. The classifier has given correct diagnosis of 90.3% (308/340) in the tested segmented regions-of-interest from typical cases and 77.5% (124/160) in tested segmented regions-of-interest from atypical cases.