Intelligent multimodal medical image fusion with deep guided filtering

Abstract

Medical image fusion is a synthesis of visual information present in any number of medical imaging inputs into a single fused image without any distortion or loss of detail. It enhances image quality by retaining specific features to improve the clinical applicability of medical imaging for treatment and evaluation of medical conditions. A big challenge in the processing of medical images is to incorporate the pathological features of the complement into one image. The fused image presents various challenges, such as existence of fusion artifacts, hardness of the base, comparison of medical image input, and computational cost. The techniques of hybrid multimodal medical image fusion (HMMIF) have been designed for pathologic studies, such as neurocysticercosis, degenerative and neoplastic diseases. Two domain algorithms based on HMMIF techniques have been developed in this research for various medical image fusion applications for MRI-SPECT, MRI-PET, and MRI-CT. NSCT is initially used in the proposed method to decompose the input images which give components of low and high frequency. The average fusion rule applies to NSCT components with low frequency. The NSCT high frequency components are fused by the law of full fusion. NSCTs high frequency is handled with directed image filtration scheme. The fused picture is obtained by taking inverse transformations from all frequency bands with the coefficients obtained from them. The methods suggested are contrasted with traditional approaches in the state of the art. Experimentation proves that the methods suggested are superior in terms of both qualitative and quantitative assessment. The fused images using proposed algorithms provide information useful for visualizing and understanding the diseases to the best of both sources’ modality.