title: The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density.
abstract: We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices.
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China ; Department of Biomedical Engineering, Sichuan Medical University, Zhongshan Road, Luzhou, Sichuan 646000, China ; Provincial Key Laboratory of Digital Media, Chengdu 611731, China.
authors.forename: Guocheng
authors.initials: G
authors.lastname: Yang
authors.affiliation: School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Meiling
authors.initials: M
authors.lastname: Li
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China ; Provincial Key Laboratory of Digital Media, Chengdu 611731, China.
authors.forename: Leiting
authors.initials: L
authors.lastname: Chen
authors.affiliation: School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology; D019295:Computational Biology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D064847:Multimodal Imaging / Q000379:methods / Q000706:statistics & numerical data; D016011:Normal Distribution; D049268:Positron-Emission Tomography / Q000706:statistics & numerical data; D015899:Tomography, Emission-Computed, Single-Photon / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data