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Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis

Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis

Rajalingam B., Priya R., Bhavani R., Santhoshkumar R.
Copyright: © 2020 |Pages: 38
ISBN13: 9781799827368|ISBN10: 1799827364|ISBN13 Softcover: 9781799827375|EISBN13: 9781799827382
DOI: 10.4018/978-1-7998-2736-8.ch007
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MLA

Rajalingam B., et al. "Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis." Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities, edited by Shouvik Chakraborty and Kalyani Mali, IGI Global, 2020, pp. 159-196. https://doi.org/10.4018/978-1-7998-2736-8.ch007

APA

Rajalingam B., Priya R., Bhavani R., & Santhoshkumar R. (2020). Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis. In S. Chakraborty & K. Mali (Eds.), Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities (pp. 159-196). IGI Global. https://doi.org/10.4018/978-1-7998-2736-8.ch007

Chicago

Rajalingam B., et al. "Image Fusion Techniques for Different Multimodality Medical Images Based on Various Conventional and Hybrid Algorithms for Disease Analysis." In Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities, edited by Shouvik Chakraborty and Kalyani Mali, 159-196. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2736-8.ch007

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Abstract

Image fusion is the process of combining two or more images to form a single fused image, which can provide more reliable and accurate information. Over the last few decades, medical imaging plays an important role in a large number of healthcare applications including diagnosis, treatment, etc. The different modalities of medical images contain complementary information of human organs and tissues, which help the physicians to diagnose the diseases. The multimodality medical images can provide limited information. These multimodality medical images cannot provide comprehensive and accurate information. This chapter proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods. The hybrid multimodal medical image fusion algorithms are used to improve the quality of fused multimodality medical image. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization.

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