A method for texture classification of ultrasonic liver images based on Gabor Wavelet

In this paper we proposed a new method for texture classification of ultrasonic liver images based on Gabor wavelet. It is well known that Gabor wavelets attain maximum joint space-frequency resolution which is highly significant in the process of texture extraction in which the conflicting objectives of accuracy in texture representation and texture spatial localization are both important. This fact has been explored in our results as it shows that the classification rate obtained by Gabor wavelet is higher that those obtained using dyadic wavelets. The feature vector consists of 10 elements at each scale from Gabor wavelets which is relatively small compared to other methods. This has a significant impact on the speed of retrieval process. The proposed algorithm applied to discriminate ultrasonic liver images into three disease states that are normal liver, liver hepatitis and cirrhosis. In our experiment 45 liver sample images from each three disease states which already proven by needle biopsy were used. We achieved the sensitivity 85% in the distinction between normal and hepatitis liver images and 86% in the distinction between normal and cirrhosis liver images. Based on our experiments, the Gabor wavelet is more appropriate than dyadic wavelets for texture classification as it leads to higher classification accuracy.