Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain.
Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image...
| Publicado en: | Journal of Digital Imaging Vol. 35; no. 5; pp. 1308 - 1326 |
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| Autores principales: | , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159758918&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159758918 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2022 vid: 35 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159758918 157710806 159758918 159758918 10.1007/s10278-021-00554-y 159758918 ppf: 1308 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multimodal Medical Image Fusion Using Stacked Auto-encoder in NSCT Domain. aug: au: Tawfik, Nahed Elnemr, Heba A. Fakhr, Mahmoud Dessouky, Moawad I. El-Samie, Fathi E. Abd affil: Computers and Systems Department, Electronics Research Institute, Joseph Tito St, El Nozha, Huckstep Cairo, Egypt sug: subj: Diagnostic Imaging Methods Image Processing, Computer Assisted Human Deep Learning Image Interpretation, Computer Assisted Magnetic Resonance Imaging Tomography, X-Ray Computed ab: Medical image fusion is a process that aims to merge the important information from images with different modalities of the same organ of the human body to create a more informative fused image. In recent years, deep learning (DL) methods have achieved significant breakthroughs in the field of image fusion because of their great efficiency. The DL methods in image fusion have become an active topic due to their high feature extraction and data representation ability. In this work, stacked sparse auto-encoder (SSAE), a general category of deep neural networks, is exploited in medical image fusion. The SSAE is an efficient technique for unsupervised feature extraction. It has high capability of complex data representation. The proposed fusion method is carried as follows. Firstly, the source images are decomposed into low- and high-frequency coefficient sub-bands with the non-subsampled contourlet transform (NSCT). The NSCT is a flexible multi-scale decomposition technique, and it is superior to traditional decomposition techniques in several aspects. After that, the SSAE is implemented for feature extraction to obtain a sparse and deep representation from high-frequency coefficients. Then, the spatial frequencies are computed for the obtained features to be used for high-frequency coefficient fusion. After that, a maximum-based fusion rule is applied to fuse the low-frequency sub-band coefficients. The final integrated image is acquired by applying the inverse NSCT. The proposed method has been applied and assessed on various groups of medical image modalities. Experimental results prove that the proposed method could effectively merge the multimodal medical images, while preserving the detail information, perfectly. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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