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Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis

Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis

Rajalingam B., Priya R., Bhavani R.
Copyright: © 2019 |Pages: 28
ISBN13: 9781522577966|ISBN10: 1522577963|EISBN13: 9781522577973
DOI: 10.4018/978-1-5225-7796-6.ch010
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MLA

Rajalingam B., et al. "Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis." Advanced Classification Techniques for Healthcare Analysis, edited by Chinmay Chakraborty, IGI Global, 2019, pp. 206-233. https://doi.org/10.4018/978-1-5225-7796-6.ch010

APA

Rajalingam B., Priya R., & Bhavani R. (2019). Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis. In C. Chakraborty (Ed.), Advanced Classification Techniques for Healthcare Analysis (pp. 206-233). IGI Global. https://doi.org/10.4018/978-1-5225-7796-6.ch010

Chicago

Rajalingam B., Priya R., and Bhavani R. "Hybrid Multimodality Medical Image Fusion Using Various Fusion Techniques With Quantitative and Qualitative Analysis." In Advanced Classification Techniques for Healthcare Analysis, edited by Chinmay Chakraborty, 206-233. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7796-6.ch010

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Abstract

In this chapter, different types of image fusion techniques have been studied and evaluated in the medical applications. The ultimate goal of this proposed method is to obtain the fused image without any loss of similar information and preserve all special features present in the input medical images. This method is used to improve the fused image quality for better diagnosis of critical disease analysis. The fused hybrid multimodal medical image should convey better visual description than the individual input images. This chapter proposes the method for multimodal medical image fusion using the hybrid fusion algorithm. The computed tomography, magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input images used for this experimental work. In this chapter, experimental results discovered that the proposed techniques provide better visualization of fused image and gives the superior results compared to various existing traditional algorithms.

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