Multi-modality medical image fusion using hybridization of binary crow search optimization

In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. In this paper, we propose an efficient medical image fusion system based on discrete wavelet...

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Bibliographic Details
Published in:Health care management science Vol. 23; no. 4; pp. 661 - 669
Main Authors: Parvathy, Velmurugan Subbiah, Pothiraj, Sivakumar
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2020
Springer Nature B.V
Subjects:
Approximation
Business and Management
Econometrics
Health Administration
Health Informatics
Management
Operations Research/Decision Theory
Optimization
Wavelet transforms
Medical image fusion
Discrete wavelet transform
Binary crow search optimization
Detailed coefficients
Approximation coefficients
Modality
Median filter
ISSN:1386-9620, 1572-9389, 1572-9389
Online Access:Get full text
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Summary:In clinical applications, single modality images do not provide sufficient diagnostic information. Therefore, it is necessary to combine the advantages or complementarities of different modalities of images. In this paper, we propose an efficient medical image fusion system based on discrete wavelet transform and binary crow search optimization (BCSO) algorithm. Here, we consider two different patterns of images as the input of the system and the output is the fused image. In this approach, at first, to enhance the image, we apply a median filter which is used to remove the noise present in the input image. Then, we apply a discrete wavelet transform on both the input modalities. Then, the approximation coefficients of modality 1 and detailed coefficients of modality 2 are combined. Similarly, approximation coefficients of modality 2 and detailed coefficients of modality 1 are combined. Finally, we fuse the two modality information using novel fusion rule. The fusion rule parameters are optimally selected using binary crow search optimization (BCSO) algorithm. To evaluate the performance of the proposed method, we used different quality metrics such as structural similarity index measure (SSIM), Fusion Factor (FF), and entropy. The presented model shows superior results with 6.63 of entropy, 0.849 of SSIM and 5.9 of FF.
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ISSN:1386-9620
1572-9389
1572-9389
DOI:10.1007/s10729-019-09492-2