  Multimodal medical image fusion helps to increase efficiency in medical
diagnosis. This paper presents multimodal medical image fusion by selecting
relevant features using Principle Component Analysis (PCA) and Particle Swarm
Optimization techniques (PSO). DTCWT is used for decomposition of the images
into low and high frequency coefficients. Fusion rules such as combination of
minimum, maximum and simple averaging are applied to approximate and detailed
coefficients. The fused image is reconstructed by inverse DTCWT. Performance
metrics are evaluated and it shows that DTCWT-PCA performs better than
DTCWT-PSO in terms of Structural Similarity Index Measure (SSIM) and Cross
Correlation (CC). Computation time and feature vector size is reduced in
DTCWT-PCA compared to DTCWT-PSO for feature selection which proves robustness
and storage capacity.
