MR Brain Image Segmentation Based on Wavelet Transform and SOM Neural Network
Magnetic resonance (MR) brain image has been accepted as the reference image in the clinical research. The goal of MR brain image segmentation is to accurately identify the principal tissue structures in the image volumes. In this paper, the segmentation algorithm based on SOM (self-organizing map) neural network with compression pre-processing by wavelet transform is presented. The compression idea origins from image pyramid structure theory, which can enhance the representation of later image feature extraction without affecting brain tissue structure information. Compared with the traditional individual SOM network method, the hybrid method can improve network training quality by applying statistical intensity information of the compression image pixels as network input vectors. Simulated MR brain images with different noise levels and intensity inhomogeneities are segmented to demonstrate the superiority of the proposed method compared to the traditional technique.
MR Brain Image Tissue Segmentation SOM Neural Network Wavelet Transform
Dan Tian Linan Fan
School of Information, Shenyang University, Shenyang, 110044
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
4243-4246
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）