Medical Image Fusion Using Redundant Wavelet Based ICA Co-Variance Analysis (International Journal of Engineering and Computer Science, 2015)
In this paper the discussion is on research work dealing with medical image fusion combination utilizing the wavelet changes occurring for images in frequency domain is discussed. We have manufactured a model framework that permits experimentation with a different wavelet which exhibit reduced blend ratio and control techniques for fusion combination, utilizing an arrangement of essential operation of ICA or independent component analysis on wavelet decomposed bands. The fusion system concentrates utilizing a wavelet condition called when a valid number of components are needed called RDWT with great information handling capacity and enhancing traits. The technique enhances a standard wavelet merger for blending the lower recurrence segments of a multi-imaged data and using deviation rules with weighting normal absolute values. The systems was tested numerically with the previous approach of PCA analysis on the basis entropy calculation, fusion factor for fused and original images, the PSNR index values, MSE metric and the STD standard deviation of the fused image. The experimental results show the superiority of the proposed system over past approach.