We have calssified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypoechoic nodular lesions. Neural network technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multi-layer feedforward neural network utilizing the back-propagation algorithm. We carried out four kinds of pre-processings for liver parenchymal pattern in the images. We describe the examination of each performance by these pre-processing techniques. We show four results using (1) only magnitudes of FFT pre-processing, (2) both magnitudes and phase angles, (3) data normalized by the maximum value in the dataset, and (4) data normalized by variance of the dataset. Among the 4 pre-processing data treatments studied, the process combining FFT phase angles and magnitudes of FFT is found to be the most efficient.

We have classified parenchymal echo patterns of cirrhotic liver into four types, according to the size of hypo echoic nodular lesions. The NN (neural network) technique has been applied to the characterization of hepatic parenchymal diseases in ultrasonic B-scan texture. We employed a multilayer feedforward NN utilizing the back-propagation algorithm. We extracted 1616 pixels in the two-dimensional regions. However, when a large area is used, input data becomes large and much time is needed for diagnosis. In this report, we used DCT (discrete cosine transform) for the feature extraction of input data, and compression. As a result, DCT was found to be suitable for compressing ultrasonographic images.

We have classified parenchymal echo patterns of cirrhotic liver into 3 types, according to the size of hypoechoic nodular lesions. We have been studying an ultrasonic image diagnosis system using the three–layer back–propagation neural network. In this paper, we will describe the applications of the neural network techniques for recognizing and classifying chronic liver disease, which use the nodular lesions in the Proton density and T2–weighed magnetic resonance images on the gray level of the pixels in the region of interest.