Multimodal image fusion and denoising in NSCT domain using CNN and FOTGV

Abstract

Multimodal medical image fusion is the most popular tool to integrate important information of multimodal medical images into a single complete informative image. Fusion provides an effective way for medical image diagnosis and treatment. However the acquired medical images may be corrupted with noise due to patient movement or faulty transmission, which misguides the image analysis and requires denoising. Therefore, in the suggested scheme non-subsampled contourlet transform (NSCT) is first used to extract features from the noisy source images. Then, a Siamese convolutional neural network (sCNN) is utilized for weighted fusion of important features from the two multimodal images. Simultaneously, a fractional order total generalized variation (FOTGV) is implemented for noise removal with improved degree of freedom. The image processing results demonstrate that the NSCT + sCNN + FOTGV scheme performs effectively for clean as well as noisy images in comparison to the other state-of-the-art conventional techniques on the basis of visual and quantitative analysis.