Multimodal medical image fusion based on Integer Wavelet Transform and Neuro-Fuzzy

Abstract

Medical image fusion is used to derive useful information from multimodality medical image data. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide precise information to the doctor and clinical treatment planning system. This paper proposes image fusion based on Integer Wavelet Transform (IWT) and Neuro-Fuzzy. The anatomical and functional images are decomposed using Integer Wavelet Transform. The wavelet coefficients are then fused using neuro-fuzzy algorithm. Then Inverse Integer Wavelet Transform (IIWT) is applied to the fused coefficients to get the fused Image. The performance of this algorithm is compared with image fusion based on Discrete Wavelet Transform (DWT) and neuro-fuzzy using entropy metric. Fusion Symmetry (FS) which quantifies the relative distance in terms of mutual information of the fused image with respect to input images is measured. Fusion Factor (FF) the criterion of maximizing the joint mutual information is also quantified.