In general, the spleen accompanied by abnormal abdomen is hypertrophied. However, if the spleen size is originally small, it is hard to detect the splenic enlargement due to abnormal abdomen by simply measure the size. On the contrary, the spleen size of a person having a normal abdomen may be large by nature. Therefore, measuring the size of spleen is not a reliable diagnostic measure of its enlargement or the abdomen abnormality. This paper proposes an automatic method to diagnose the splenic enlargement due to abnormality, by examining the boundary pattern of spleen in abdominal CT images.

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.