In this paper, the classification of ultrasonic liver images is presented. By combining texture features extracted by two methods, including co-occurrence matrix and local texture structure frequency, the system can classify three liver states, which are normal liver, hepatitis and cirrhosis. The co-occurrence method extracts texture features which represent gray level variation of pixel pair under a specific spatial relationship. The local texture structure frequency, we have proposed new texture descriptor, extract local surface structure of a small texture unit. Several texture features are derived from this texture frequency, which include coarseness, homogeneity, structure stability, etc., A forward sequential search process is adopted to look for the most useful texture features from co-occurrence matrix and our proposed method. These textures features extracted from the two methods are then fed into a probabilistic neural network to classify the liver states. The system has been implemented on a Sun sparc II workstation and tested with sixty ultrasonic images. The correct classification rate is around 88%. These results suggest that the selected texture features are effective for the classification of liver echotexture.
|Number of pages||8|
|Journal||Biomedical Engineering - Applications, Basis and Communications|
|Publication status||Published - 1995|
All Science Journal Classification (ASJC) codes