Enhancement of Hybrid Multimodal Medical Image Fusion Techniques for Clinical Disease Analysis

Abstract

Multimodal medical image fusion is one the most significant and useful disease analytic techniques. This research article proposes 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. Magnetic resonance imaging, positron emission tomography, and single photon emission computed tomography are the input multimodal therapeutic images used for fusion process. An experimental result of proposed hybrid fusion techniques provides the fused multimodal medical images of highest quality, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with existing techniques the proposed result gives the better processing performance in both qualitative and quantitative evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.