Medical image fusion algorithm based on texture extraction by means of bidimensional empirical mode decomposition

ZHANG Baohua; LIU He; ZHANG Chuanting

doi:10.7510/jgjs.issn.1001-3806.2014.04.007

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.

Medical image fusion algorithm based on texture extraction by means of bidimensional empirical mode decomposition

1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China

Received Date: 2013-09-11

Accepted Date: 2013-10-10

Available Online: 2014-07-25

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.

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Reference (14)

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Medical image fusion algorithm based on texture extraction by means of bidimensional empirical mode decomposition

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