Feature Selection based on PCA and PSO for Multimodal Medical Image Fusion using DTCWT
This work addresses efficiency in medical diagnosis through image fusion, but it is incremental as it combines existing methods like PCA, PSO, and DTCWT.
The paper tackled multimodal medical image fusion by selecting features using PCA and PSO with DTCWT decomposition, finding that DTCWT-PCA outperformed DTCWT-PSO in SSIM and CC metrics while reducing computation time and feature vector size.
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.