CLASSIFICATION OF ULTRASONIC IMAGES USING FUZZY-REASONING AND SPATIAL SMOOTHING EFFECT OF TEXTURAL FEATURES

This paper discusses the method for the semiautomatic discrimination of the biological tissue and organs, based on the medial ultrasonic data. The ultrasonic image is difficult to be evaluated on the absolute basis, since multiple physical information is included simultaneously and the image is affected greatly by the individuality of the object. This method utilizes the feature parameters of the image for various tissues, and the discrimination is executed on the relative basis by comparing the feature parameters. In the past method, the general procedure is to utilize the first-order statistics such as the average image density. In the case of a tumor in the liver, for example, there does not exist a remarkable density difference between the image of the tumor and the surrounding normal tissues, which prevents a satisfactory discrimination of the tumor and other images. This paper aims at the discrimination of tissue images with small density differences, based on the textural feature parameters (the second-order statistics) contained in the image. However, the crisp-like discrimination is difficult since there is an overlapping ambiguous area between the histograms of the feature parameters of the tissues. To improve the discrimination rate for the ultrasonic image, a method is proposed which utilizes the nonlinear input-output relation of the fuzzy inference and the spatial distribution of the image to be adapted to each object. Using the data obtained from the test phantom for the ultrasonic diagnostic equipment and the biological data obtained from four subjects, the effectiveness of the proposed method is demonstrated.