EBFS-ICA: An Efficient Algorithm for CT-MRI Image Fusion

Abstract

Analyzing the spatial and spectral properties of CT and MRI scan medical images; this article proposes a novel method for CT-MRI image fusion. Independent component analysis is used to analyze images for acquiring independent component. This paper addresses an efficient algorithm for ICA-based image fusion with selection of optimal independent components using E-coli Bacterial Foraging Optimization Technique. Different methods were suggested in the literature to select the largest eigenvalues and their corresponding eigenvectors for ICA based image fusion. But, there is no unified approach for selecting optimal ICA bases to improvise the performance. In this context, we propose a new algorithm called EBFS-ICA which uses a nutrient concentration function (cost function). Here the cost function is maximized through hill climbing via a type of biased random walk. The proposed EBFS-ICA algorithm offers two distinct additional advantages. First, the proposed algorithm can supplement the features of ICA. Second, the random bias incorporated in EBFS guide us to move in the direction of increasingly favorable environment. Finally, we use fusion rules to generate the fused image which contain more integrated accurate detail information of different soft tissue such as muscles and blood vessels. Experimental results presented here show the effectiveness of the proposed EBFS-ICA algorithm. Further, the efficiency of our method is better than FastICA method used in medical image fusion field.