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International Journal of Biomedical Imaging
Volume 2017, Article ID 3020461, 11 pages
Research Article

Medical Image Fusion Based on Feature Extraction and Sparse Representation

1Xi’an Institute of Optics and Precision Mechanics, Chinese Academic of Sciences, Xi’an 710119, China
2University of Chinese Academy of Sciences, Beijing 100049, China

Correspondence should be addressed to Yin Fei; nc.tpo@iefniy

Received 31 August 2016; Revised 1 January 2017; Accepted 10 January 2017; Published 21 February 2017

Academic Editor: Jyh-Cheng Chen

Copyright © 2017 Yin Fei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.