Title: Medical image fusion using optimal feature selection methods based on second generation contourlet transform

Authors: Yujie Li; Huimin Lu; Ling Chen; Seiichi Serikawa

Addresses: Department of Electrical Engineering and Electronics, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan ' Japan Society for the Promotion of Science, Tokyo 102-0083, Japan ' School of Information Engineering, Yangzhou University, Yangzhou 225-009, China ' Department of Electrical Engineering and Electronics, Kyushu Institute of Technology, Kitakyushu 804-8550, Japan

Abstract: As a novel of multi-resolution analysis tool, second generation contourlet transform (SGCT) provides flexible multiresolution, anisotropy, and directional expansion for medical imaging systems. In this paper, a novel fusion method for multimodal medical images based on SGCT is proposed. Firstly, we utilise the SGCT to decompose the multimodal medical images with highpass subbands and lowpass subbands. Then, for the highpass subbands, the weighted sum modified Laplacian (WSML) method is utilised to generate the high frequency coefficients to recovery image details. For the lowpass subbands, the maximum local energy (MLE) method is combined with 'local patch' idea for low frequency coefficients selection. Finally, the fused image is obtained by applying inverse SGCT to combine lowpass and highpass subbands. During abundant experiments, we evaluate the proposed method both human visual and quantitative analysis. Compare with the-state-of-the-art methods, the new strategy for attaining image fusion with satisfactory performance.

Keywords: medical images; image fusion; sum modified Laplacian; maximum local energy; MLE; second generation contourlet transform; SGCT; medical imaging systems; multimodal images.

DOI: 10.1504/IJAACS.2015.069560

International Journal of Autonomous and Adaptive Communications Systems, 2015 Vol.8 No.2/3, pp.306 - 319

Received: 23 Jan 2013
Accepted: 13 Apr 2013

Published online: 27 May 2015 *

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