Open AccessMultimodal Medical Image Fusion Using Various Hybrid Fusion Techniques For clinical Treatment Analysis
Author(s) -
B Rajalingam,
R Priya
Publication year - 2018
Publication title -
smart construction research
Language(s) - English
Resource type - Journals
ISSN - 2529-7740
DOI - 10.18063/scr.v0.594
Subject(s) - image fusion , multimodality , computer science , artificial intelligence , fusion , visualization , positron emission tomography , medical imaging , feature (linguistics) , image processing , process (computing) , computer vision , pattern recognition (psychology) , image (mathematics) , radiology , medicine , linguistics , philosophy , world wide web , operating system
Medical image fusion is one the most significant and useful disease analytic techniques. This research paper proposed and examines some of the hybrid multimodality medical image fusion methods and discusses the most essential advantages and disadvantages of these methods to develop hybrid multimodal image fusion algorithms that improve the feature of merged multimodality therapeutic image. Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography and Single Photon Emission Computed Tomography are the input multimodal therapeutic images used for fusion process. An experimental results of proposed all hybrid fusion techniques provides the best fused multimodal medical images of highest quality, highest details, shortest processing time, and best visualization. Both traditional and hybrid multimodal medical image fusion algorithms are evaluated using several quality metrics. Compared with other existing techniques the proposed technique experimental results demonstrate the better processing performance and results in both subjective and objective evaluation criteria. This is favorable, especially for helping in accurate clinical disease analysis.