Water wave optimized nonsubsampled shearlet transformation technique for multimodal medical image fusion

Medical image fusion has advanced to the point that it is now possible to combine multiple medical images for accurate disease diagnosis and treatment. The state-of-art techniques based on spatial and transform domains suffer from different limitations such as low fused image quality, spectral degradation, contrast reduction, low edge information preserving, lack of shift-invariance, high computational complexity, classification accuracy, and sensitivity to noise. The main motivation of this work is to generate a single image with excellent visual clarity that retains the features of the source images. This article proposes a water wave optimized nonsubsampled shearlet transformation technique (NSST) for multimodal medical image fusion, in which the water wave optimization (WWO) algorithm is used to allocate the weights of the NSST approach's high-frequency subbands. The NSST approach is primarily used in this work due to its ability to withstand shift-invariance and its potential to improve the visual clarity of the fused multimodal image by preserving the essential features present in the image's various directions and edges. We combined the NSST technique with the WWO algorithm, which processes the edges, details, and contourlets of medical images using a max selection strategy based on the fitness function, to improve image quality and computational costs. The WWO algorithm is mainly applied to the NSST to minimize the L1 distance between the fused and the source images. Hence to overcome this problem a condition CNN optimized with a hybrid tunicate swarm memetic (TSM) algorithm is used to incorporate both the benefits offered by the condition CNN-TSM algorithm and NSST. The TSM optimized condition CNN architecture is used to preserve the coefficients of the image and improve the perceiving capability of the high-frequency sub-bands. An inverse NSST is used for fused frequency sub-band integration. Finally, the efficiency of the proposed methodology is evaluated in terms of enhanced visual feature quality, edge detection, contour detection, and computational performance.