Intelligent analysis of prostate ultrasound images
In this paper, we present an intelligent approach to analysing prostrate ultrasound images in order to diagnose prostate cancer. Algorithms based on fuzzy image processing are applied first to enhance the contrast of the original image, to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterising the underlying texture of the regions of interest based wavelet domain features. Finally, a rough neural network, where a neural network and rough set theory are integrated into a hybrid system, is designed for discrimination of different regions of interest to test whether they represent malignant or benign cases. The neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bound concepts of rough set theory. Experimental results show that the overall classification accuracy of our approach is high.