CT and MRI image fusion using wiener filter in dual tree framework

Multimodal medical image fusion is the fusion of images belonging to different modalities like, CT, MRI, PET, X-Ray etc. The purpose is to yield an image more efficient for diagnosis while enhancing the essential information extracted from the images acquired using different sensors. In this paper, the multimodal CT and MRI images are fused using Dual tree Discrete Wavelet transform. Discrete Wavelet transform (DWT) decomposes the image into wavelet coefficients without effecting the image information. The wavelet coefficients are then fused by employing Principal Component analysis (PCA) for approximation coefficients and maximum selection rule for detailed coefficients to enhance contrast. The major advantage of using PCA is to minimize redundancy from the image and to get a highly focused fused image. The simulation results of improved framework show better performance over existing methods in terms of entropy, mutual information, and fusion factor.