NSCT-FS:A Hybrid Fusion Approach Combining NSCT, and Fuzzy Sets for Multimodal Medical Image Fusion

Abstract

Disease diagnosis and clinical treatment involve the combination of medical images, which is one of the major technologies in the medical field. As per literature analysis, the traditional methods have some limitations for generating the fused image, including low contrast, uncertainty, and distorted sides. To address these issues, we suggest a hybrid method of image fusion using NSCT (non-subsampled contourlet transform) and fuzzy sets. Firstly, the source images were fuzzified via a normalization process. After that, the fuzzy images are decomposed into approximation and detail layers at the various scales using multi-scale decomposition, i.e., NSCT. Secondly, the maximum and local variance-based rules are used to extract the significant structural and edge details from approximation and detail coefficients, respectively. Thirdly, the reconstruction process is carried out to achieve the final fused image, followed by the defuzzification process. This approach demonstrates the efficiency of the proposed fusion process with visual analysis, including different existing algorithms. Furthermore, the quantitative analysis proves the effectiveness of the proposed model using various quality metrics such as mean, standard deviation, and average gradient.