Medical Image Fusion Using a New Entropy Measure Between Intuitionistic Fuzzy Sets Joint Gaussian Curvature Filter

Abstract

Medical image fusion can provide a synthetical presentation of human tissue by fusing the complementary features of different source images, such as computerized tomography (CT) and magnetic resonance imaging (MRI), which contributes to clinical diagnosis and treatment. How to measure the key features of medical image is the most important issue in image fusion, intuitionistic fuzzy set (IFS) is a classical computation theory that can be employed to handle the task of feature extraction. In this work, we propose a medical image fusion method based on a new entropy measure of IFSs joint Gaussian curvature filter (GCF). First, GCF is employed to separate the medical image into a set of detailed sub-images and a base sub-image. Second, the detailed sub-images is transformed into IFSs, so that the entropy measure of IFSs is then used to present image features. Third, two specific fusion rules are respectively utilized to integrate the detailed and base sub-images. Finally, the fused image is produced by the fused sub-images based on the regulation of GCF. Several specific experiments are performed on our entropy measure to test the performance, and the existing image fusion methods are also performed to verify the performance of our image fusion method.