To develop a smoothing method for speckle reduction is a significant problem, because of the complex ultrasonic characteristics and the obscurity of the tissue image. This paper presents a new method for speckle reduction from medical ultrasonic image by using fuzzy morphological speckle reduction algorithm (FMSR) that preserves resolvable details while removing speckle in order to cope with the ambiguous and obscure ultrasonic images. FMSR creates a cleaned image by recombining the processed residual images with a smoothed version of an original image. Performance of the proposed method has been tested on the phantom and tissue images. The results show that the method effectively reduces the speckle while preserving the resolvable details.

An image obtained by ultrasonic medical equipment is poor in quality because of speckle noise, that is caused by the quality of ultrasonic beam and so on. Thus, it is very difficult to detect internal organs or the diseased tissues from a medical ultrasonic image by the processing, which is used only gray-scale of the image. To analyze the ultrasonic image, it is necessary to use not only gray-scale but also appropriate statistical character. In this paper, we suggest a new method to extract regions of internal organs from an ultrasonic image by the discrimination function. The discrimination function is based on gray-scale and statistical characters of the image. This function is determined by using parameters of the multi-dimensional autoregressive model.

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.