Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer’s disease

Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image whi...

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Bibliographic Details
Published in:Review of scientific instruments 2016-07, Vol.87 (7), p.074303-074303
Main Authors: Bhateja, Vikrant, Moin, Aisha, Srivastava, Anuja, Bao, Le Nguyen, Lay-Ekuakille, Aimé, Le, Dac-Nhuong
Format: Article
Language:English
Subjects:
ALGORITHMS
Alzheimer Disease - diagnostic imaging
BRAIN
Brain - diagnostic imaging
Coefficients
Color
COMPARATIVE EVALUATIONS
Computer vision
CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
Defense industry
DIAGNOSIS
Diagnosis, Computer-Assisted - methods
DISEASES
ENTROPY
Entropy (Information theory)
High frequencies
Humans
Image processing
Image Processing, Computer-Assisted
INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
Low frequencies
Medical imaging
NONLINEAR PROBLEMS
Positron-Emission Tomography
Principal components analysis
Scientific apparatus & instruments
Single Photon Emission Computed Tomography Computed Tomography
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Summary:Computer based diagnosis of Alzheimer’s disease can be performed by dint of the analysis of the functional and structural changes in the brain. Multispectral image fusion deliberates upon fusion of the complementary information while discarding the surplus information to achieve a solitary image which encloses both spatial and spectral details. This paper presents a Non-Sub-sampled Contourlet Transform (NSCT) based multispectral image fusion model for computer-aided diagnosis of Alzheimer’s disease. The proposed fusion methodology involves color transformation of the input multispectral image. The multispectral image in YIQ color space is decomposed using NSCT followed by dimensionality reduction using modified Principal Component Analysis algorithm on the low frequency coefficients. Further, the high frequency coefficients are enhanced using non-linear enhancement function. Two different fusion rules are then applied to the low-pass and high-pass sub-bands: Phase congruency is applied to low frequency coefficients and a combination of directive contrast and normalized Shannon entropy is applied to high frequency coefficients. The superiority of the fusion response is depicted by the comparisons made with the other state-of-the-art fusion approaches (in terms of various fusion metrics).
ISSN:0034-6748
1089-7623
DOI:10.1063/1.4959559