A Brain MR Images Segmentation Method Based on SOM Neural Network
Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. In this paper, a novel brain MR images segmentation method is presented based on self-organizing map (SOM) neural network. The method comprises two main steps: feature extraction and pixel classification based on SOM neural network. In traditional techniques, neural nctworks input is the feature vector extracted from the intensity of the pixel and of its n nearest neighbors, which introduces dependency on the gray levels spatial distribution, and thus the final segmentation results are prone to be effected by noise. To enhance the robustness of the method, we perform statistical transformation to the traditional feature vector as neural networks input. Simulated brain MR images with different noise levels and intensity inhomogeneities are segmented to demonstrate the superiority of the proposed method compared to the traditional technique.
MR images tissue segmentation SOM neural network statistical transformation
Dan Tian Linan Fan
School of Information Shenyang University Shenyang, China
国际会议
武汉
英文
698-701
2007-07-06（万方平台首次上网日期，不代表论文的发表时间）