激光技术Issue(4)：463-468,6.DOI:10.7510/jgjs.issn.1001-3806.2014.04.007
基于经验模态分解提取纹理的图像融合算法
Medical image fusion algorithm based on texture extraction by means of bidimensional empirical mode decomposition
摘要
Abstract
In order to improve the quality of medical fusion images , a novel medical image fusion algorithm based on bidimensional empirical mode decomposition ( BEMD ) feature classification and multi-pulse coupled neural network was proposed.Firstly, the multimodal medical images were decomposed into two-dimensional intrinsic mode functions (BIMF) and the residuals by means of BEMD , and then the BIMF layer and the residuals coefficients were put into pulse coupled neural network ( PCNN) to get their firing maps .The pixels with the same firing times were extracted and classified .The pixels with larger firing times were classified as texture and the rest were classified as the background .The extreme values of the texture collection were counted to determine the grayscale pixel distribution .Finally the pixels representing the texture were input into the PCNN and the other pixels were put into the dual-channel PCNN to get fusion coefficients .The experimental results show that the proposed algorithm has solved the problem of PCNN with superior performance comparing to the traditional fusion algorithms , which can improve the quality of the fused image .关键词
图像处理/医学图像融合/2维经验模态分解/2维内蕴模函数/脉冲耦合神经网络/特征提取Key words
image processing/medical image fusion/bidimensional empirical mode decomposition/bidimensional intrinsic mode functions/pulse coupled neural network/feature extraction分类
信息技术与安全科学引用本文复制引用
张宝华,刘鹤,张传亭..基于经验模态分解提取纹理的图像融合算法[J].激光技术,2014,(4):463-468,6.基金项目
国家自然科学基金资助项目 （）
