{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T10:36:47Z","timestamp":1772707007658,"version":"3.50.1"},"reference-count":21,"publisher":"AIP Publishing","issue":"7","content-domain":{"domain":["pubs.aip.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2016,7,1]]},"abstract":"<jats:p>Computer based diagnosis of Alzheimer\u2019s 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\u2019s 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).<\/jats:p>","DOI":"10.1063\/1.4959559","type":"journal-article","created":{"date-parts":[[2016,7,29]],"date-time":"2016-07-29T17:00:18Z","timestamp":1469811618000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":23,"title":["Multispectral medical image fusion in Contourlet domain for computer based diagnosis of Alzheimer\u2019s disease"],"prefix":"10.1063","volume":"87","author":[{"given":"Vikrant","family":"Bhateja","sequence":"first","affiliation":[{"name":"Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC) 1 , Lucknow, Uttar Pradesh 226028, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aisha","family":"Moin","sequence":"additional","affiliation":[{"name":"Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC) 1 , Lucknow, Uttar Pradesh 226028, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anuja","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC) 1 , Lucknow, Uttar Pradesh 226028, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le Nguyen","family":"Bao","sequence":"additional","affiliation":[{"name":"Duytan University 2 , Danang 550000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aim\u00e9","family":"Lay-Ekuakille","sequence":"additional","affiliation":[{"name":"University of Salento 3 Department of Innovation Engineering, , Lecce 73100, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2601-2803","authenticated-orcid":false,"given":"Dac-Nhuong","family":"Le","sequence":"additional","affiliation":[{"name":"Duytan University 2 , Danang 550000, Vietnam"},{"name":"Haiphong University 4 , Haiphong 180000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"317","published-online":{"date-parts":[[2016,7,29]]},"reference":[{"key":"2023070402485885700_c1","first-page":"1","article-title":"PET and MRI brain image using wavelet transform with structural information adjustment and spectral information patching","year":"2014"},{"key":"2023070402485885700_c2","first-page":"1603","article-title":"Effective image fusion method to study Alzheimer\u2019s disease using MR, PET images","year":"2015"},{"key":"2023070402485885700_c3","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.bspc.2014.11.009","article-title":"Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network","volume":"18","year":"2015","journal-title":"Biomed. 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