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Content Provider | IET Digital Library |
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Author | Singh, Sneha Anand, Radhey Shyam Gupta, Deep |
Abstract | The fusion of multimodal medical information is considered as an assisted approach for the medical professionals. Computed tomography and magnetic resonance (CT–MR) medical image fusion are able to help the radiologist in precise diagnosis of disease and deciding the required treatment in accord with the patient's condition. Therefore, a cascaded framework is proposed in this study that presents a fusion approach for multimodal medical information in ripplet transform (RT) and non-subsampled shearlet (NSST) domain. The RT and NSST having different features are utilised in a cascade manner that provides several directional decomposition coefficients and increases shift invariance information in the fused images. At the first stage decomposition, a biologically inspired neural model, motivated by novel sum-modified Laplacian and spatial frequency is utilised to fuse the low- and high-frequency coefficients, respectively, and the max fusion rule based on regional energy is utilised at stage 2. This model is used to preserve the redundant information also. The fusion performance is also validated by extensive simulations performed on different CT–MR image datasets of different diseases. Experimental results demonstrate that the proposed method provides better fused images in terms of visual quality along with the quantitative indices compared with several existing fusion approaches. |
Starting Page | 696 |
Ending Page | 707 |
Page Count | 12 |
ISSN | 17519659 |
Volume Number | 12 |
e-ISSN | 17519667 |
Issue Number | Issue 5, May (2018) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-ipr/12/5 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0214 |
Journal | IET Image Processing |
Publisher Date | 2018-01-09 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Biologically Inspired Neural Model Biology And Medical Computing Biomedical Imaging/measurement Biomedical Magnetic Resonance Imaging Biomedical MRI Cascaded Framework Computed Tomography Computer Vision And Image Processing Technique Computerised Instrumentation Computerised Tomography CT Image Information Fusion Scheme Decomposition Directional Decomposition Coefficient Disease Diagnosis Diseases First Stage Decomposition Function Theory And Analysis Image Fusion Integral Transforms Magnetic Resonance Max Fusion Rule Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy MR Image Information Fusion Scheme Multimodal Medical Information Fusion Scheme Nonsubsampled Shearlet Domain NSST Domain Optical, Image And Video Signal Processing Patient Diagnosis Patient Diagnostic Method And Instrumentation Radiography And Computed Tomography Radiologist Radiology Ripplet Transform RT Shift Invariance Information Spectroscopy Sum-modified Laplacian Model Transform X-Ray Technique X-Rays And Particle Beam |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software |
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