Ultrasound image segmentation by spectral clustering algorithm based on the curvelet and GLCM features

This paper address the issue of how to segmentation ultrasound image pathological region and propose a novel ultrasound image segmentation method by spectral clustering algorithm based on the curvelet and GLCM features. Firstly ultrasound image are subdivided into continuous small regions and each sub-region using curvelet transform and GLCM approach to get a series of feature vectors, including such as angle second-order moments, contrast, correlation, entropy, variance, mean, and the deficit moments etc; Secondly, a set of sampling pixels are selected to simplified data space and reduces the data dimension of spectral clustering algorithm. The small sample extraction method was designed to reduce the complexity of spectral clustering algorithm; Finally, priori classification of spectral clustering result as a guide, the remaining image data samples are classified using KNN method to complete the segmentation. Experimental results show that our method for pathological areas in the ultrasound image segmentation is highly accurate and effective.