MULTIMODALITY MEDICAL IMAGE FUSION USING BLOCK BASED INTUITIONISTIC FUZZY SETS

Image fusion combines more than one image from various environments into a single image. This can be useful for subsequent processing of the image, especially in medical imaging where it can help in disease diagnosis. This paper uses the block based Intuitionistic Fuzzy Sets(IFS) to fuse the multimodality medical images. IFSs can effectively handle the inherent uncertainties of digital images. Initially, in this model, entropy is used to deduce the optimal parameter value for defining the membership and non- membership function. This, in turn generates the Intuitionistic Fuzzy Images (IFI) from the original image. Finally, the IFIs are partitioned into image blocks and then recombined by the generated membership function. This paper compares the proposed method with popular ones like Principal Component Analysis (PCA), simple averaging (AVG), Laplacian Pyramid Approach(LPA), Discrete Wavelet Transform (DWT) and MPA (Morphological Pyramid Approach) on various performance measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Universal Image Quality Index (UIQI), Mean and Standard Deviation (STD). The experimental results show better image visualization generated through the proposed method compared to the other methods, in overall.