Medical image fusion using combination of PCA and wavelet analysis

Abstract

Medical image fusion for merging of complementary diagnostic content has been carried out in this paper using Principal Component Analysis (PCA) and Wavelets. The proposed fusion approach involves sub-band decomposition using 2D-Discrete Wavelet Transform (DWT) in order to preserve both spectral and spatial information. Further, PCA is applied on the decomposed coefficients to maximize the spatial resolution. An optimal variant of the daubechies wavelet family has been selected experimentally for better fusion results. Simulation results demonstrate an improvement in visual quality of the fused image in comparison to other state-of-art fusion approaches.