Comparative Analysis of Similarity Measure Performance for Multimodality Image Fusion using DTCWT and SOFM with Various Medical Image Fusion Techniques

Objectives: In this paper, the performance of similarity measures such as Edge Based Similarity Measure and Structural Similarity Index Measure is evaluated and also compared with the existing medical image fusion techniques. Materials and Methods: Multimodality Medical Image fusion is the process of fusing two Medical images obtained from two different sensors for better diagnosis. Medical image fusion combines and merges all relevant and complementary information from multiple source images into single composite image which facilitates more precise diagnosis and better treatment. The fused image should convey a better description of the scene than the individual images. The performance of the fused image is evaluated by various metrics such as Peak Signal to Noise Ratio (PSNR), Entropy, Standard deviation, Edge Based Similarity Measure (EBSM) and Structural Similarity Index Measure (SSIM). This paper proposes a method for fusion of Medical images using Dual Tree Complex Wavelet Transform (DTCWT) and Self Organizing Feature Map (SOFM). Findings: The performance of the proposed fusion algorithm is evaluated over pairs of CT and MR images obtained from patients in comparison with existing fusion techniques such as Discrete Wavelet Transform (DWT), Nonsubsampled Contourlet Transform (NSCT) and Fast Discrete Curvelet Transform (FDCT). In this paper, performance is evaluated by using the metric; Edge based Similarity Measure (EBSM), and Structural Similarity Index (SSIM). Applications / Improvements: Through the simulation result, as compared with the DWT, FDCT, NSCT and DTCWT fusion methods, it is concluded that the Multimodality image fusion using DTCWT with Robust Second Order First Moment (SOFM) gives better Edge based similarity measure and Structural similarity index measure.