Semantic Medical Image Fusion based on NSST Domain

The details or the main point provided by divergent source medical images procedure into a single combined image is represented by medical image fusion. The single combined image gives extra medical data which would be more useful in the treatment and diagnosis of a patient and for the study of various diseases. It is very important for the clinicians to completely analyze patient information from different modalities. The overall data of divergent medical images procedure is furnished by medical image fusion. A medical image fusion supported nonsubsampled shearlet transform domain is presented during an analysis of the paper. However, the model passes over the semantics of an image, which makes the clinical very hard to know the fused medical images. In this research paper will solve the problem of semantic loss of the fussed images by providing a new assessment parameter to calculate the semantic loss. The final result is very promising: The assessment of the final result shows the Excellence achievement in Quantitative and visual and lessen the semantic data loss and has a better optical result of the present medical image fusion.