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

Velmurugan Subbiah Parvathy () and Sivakumar Pothiraj ()
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Velmurugan Subbiah Parvathy: Kalasalingam Academy of Research and Education
Sivakumar Pothiraj: Kalasalingam Academy of Research and Education

Health Care Management Science, 2020, vol. 23, issue 4, No 13, 669 pages

Abstract: Abstract 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.

Keywords: Modality; Binary crow search optimization; Medical image fusion; Median filter; Discrete wavelet transform; Approximation coefficients; Detailed coefficients (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10729-019-09492-2

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