A Novel Method of Multimodal Medical Image Fusion Based on Hybrid Approach of NSCT and DTCWT

Abstract

The approach of multimodal medical image fusion, which extracts complementary information from several multimodality medical pictures, is one of the most significant and beneficial illness study tools. This work proposed an effective strategy for multimodal medical picture fusion based on a hybrid approach of NSCT and DTCWT. The experimental study's input multimodality medical images included computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). A suggested approach employs a convolutional network to generate a weight map that incorporates pixel movement information from dual or more multimodality medical pictures. To provide greater visual comprehension by humans, the medical picture fusion method is performed on a multiscale basis using medical image pyramids. Additionally, a local comparison-based method is employed to adaptively alter the fusion mode for the decomposed coefficients. The proposed fusion methodologies result in the highest-quality fused multimodal medical pictures, the lowest processing period, and the finest visualization in terms of visual quality and objective assessment standards.