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Multimodal medical image fusion helps to increase efficiency in medicaldiagnosis. This paper presents multimodal medical image fusion by selectingrelevant features using Principle Component Analysis (PCA) and Particle SwarmOptimization techniques (PSO). DTCWT is used for decomposition of the imagesinto low and high frequency coefficients. Fusion rules such as combination ofminimum, maximum and simple averaging are applied to approximate and detailedcoefficients. The fused image is reconstructed by inverse DTCWT. Performancemetrics are evaluated and it shows that DTCWT-PCA performs better thanDTCWT-PSO in terms of Structural Similarity Index Measure (SSIM) and CrossCorrelation (CC). Computation time and feature vector size is reduced inDTCWT-PCA compared to DTCWT-PSO for feature selection which proves robustnessand storage capacity.

Feature Selection based on PCA and PSO for Multimodal Medical Image Fusion using DTCWT