Image Segmentation Based on Random Neural Network Model and Gabor Filters

Image segmentation is a fundamental image process technique and plays an essential role in ultrasound image analysis. In this article, we propose an algorithm for image segmentation which is based on the random neural network (RNN) and features extracted by a bank of Gabor filters. With the scientists' work, it is revealed that Gabor functions act as some functions of human vision. And the RNN model proposed by Gelenbe is closer to biophysical reality and mathematically more tractable, in which signals in the form of impulses are transmitted with a certain probability. The segmentation algorithm based on these two techniques provide a good distinguish and classification capability for textures in the image. Furthermore, a strategy which is named as quartered segmentation strategy is also presented here in order to reduce the computation and speed up our approach. The presented algorithm is tested on an image produced by using Brodatz album and an ultrasound image, and the results are promising