Multi-modal medical image fusion in NSST domain for internet of medical things

The Internet of Medical Things (IoMT) has included a new layer for development and smart infrastructure growth in the medical field. Besides, the medical data on IoMT systems are constantly expanding due to the rising peripherals in the health system. This paper introduces a new fusion technique in the shearlet domain to improve existing methods, which may provide medical image fusion in the IoMT system. In this paper, firstly low and high frequencies NSST coefficients are obtained of both input images. Over the low frequency component, a new Multi local extrema (MLE) based decomposition is performed to get more detail features (Coarse and detail layers). Over these MLE features saliency based weighted average is performed using co-occurrence filter to get the enhanced low frequency NSST Coefficients. These enhanced low frequency NSST Coefficients of both input images are fused using the proposed weighted function. In high frequency NSST Coefficients, local type-2 fuzzy entropy-based fusion is performed. Finally, inverse NSST is performed to get the final fused image. The experimental results are evaluated and compared with existing methods by visually and also by performance metrics. After a critical analysis, it was found that the results of the proposed method give better outcomes compared to similar and recent existing schemes.