Abstract :
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.
Keyword :
Multimodal medical image fusion, MRI, PET, SPECT, PCA, DCT, DWT, DCHWT, GIF, curvelet transform, Sub-band Decomposition, Ridgelet Transform, PCNN, Neuro-Fuzzy