Quantitative Analysis based on Supervised Classification of Medical Image Fusion Techniques

Abstract

Fusion can be described as the process of integrating information resulting from the collection of two or more images from different sources to form a single integrated image. This image will be more productive, informative, descriptive and qualitative as compared to original input images or individual images. Fusion technology in medical images is useful for the purpose of diagnosing disease and robot surgery for physicians. This paper describes different techniques for the fusion of medical images and their quality studies based on quantitative statistical analysis by studying the statistical characteristics of the image targets in the region of the edges and studying the differences between the classes in the image and the calculation of the statistical scale (mode) between the classes in the region of the edges before and after fusion. The results proved highly efficient in the integration of medical information and increase the sharping of contrast, force the separation and show the fine details between the classes.