Abstract
Multimodality medical image fusion is a core area of exploration in the clinical sector due to its ability to provide an output image of high quality for better clinical diagnosis and treatment. Medical imaging modalities like CT, MRI, PET, and SPECT play a huge role in the health sector. Each of these modalities offers only specific information that may be suitable for the detection and analysis of specific diseases. Multimodality medical image fusion is employed to coalesce the pertinent information present in two images on a single image, rendering clinical analysis and diagnosis considerably easier for doctors. This paper gives a thorough review of various techniques for multimodality medical image fusion over recent years in chronological order. A comparative study of various image fusion algorithms with their pros and cons is listed in the paper. This paper also compiles different methods that are being used by researchers for the fusion of multimodality medical images.
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