An Efficient Approach For Medical Image Fusion Using Sparse Representation Model

Abstract

Multimodal Medical Image Sensor Fusion provides better visualization by integrating the image information from different medical image modalities. It plays a vital role in the precise diagnosis of very critical diseases in medical field. Generally, images acquired from different imaging modalities are downgraded due to noise interference that leads to false diagnosis in medical images. This paper presents a fusion framework for MRI-PET images, that captures the subtle details of an input images. First, the input images are decomposed by Non-Sampled Shearlet Transform (NSST) into low frequency (L_F) and high frequency (H_F) components to separate the basic and edge details. Second, the sparse representation-based model is used to merge the L_Fcomponents and H_Fcomponents are fused with Gradient-Domain Guided filtering approach. Finally, the reconstruction of fused images is employed using inverse NSST. The experimental results based on MRI and PET images database shows that the proposed approach produces good visually fused medical images with better computation measures.