Directive clustering contrast-based multi-modality medical image fusion for smart healthcare system

Smart healthcare is being adopted gradually as information technology advances. The enormous increase in demand for smart medical imaging has resulted in the fusion of a number of important imaging technologies. In smart imaging, many times single modality images are not sufficient to extract the major or minor information from medical images. Therefore in this paper, a new fusion algorithm is introduced for multi-modality medical images to extract maximum information and provide an efficient fused image. In proposed scheme, NSCT is used to get low- and high-frequency components of the medical images. Further, clustering-based fusion technique is used for fusing low-frequency components by analysing cluster features. Similarly, contrast-preserving image fusion on the high-frequency coefficients is accomplished by the use of directed contrast based on cluster-based components. The experimental results and comparison analysis is conducted on the multi-modal medical image dataset. Test results and evaluations of the proposed technique show that it outperforms the leading fusion strategies in terms of contrast and edge preservations.