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Dual Tree Complex Wavelet Transform and Robust Organizing Feature Map in Medical Image Fusion Technique

Abstract-Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) techniques in medical image has revolutionized for medical diagnosis. From CT coordinate system, Intensity based image registration is used in MRI image. Image fusion is an advanced tool in medical imaging. To make a medical diagnosis easier and accurate, image fusion combines MRI and CT images into a single image, which contains the relevant information from the original source images. Image fusion technique is based on Dual Tree Complex Wavelet Transform (DT-CWT) and Robust Self Organizing Feature Mapping (Robust SOFM) neural network. DT_CWT decomposes the images in a multi-scale and multi-direction to extract important features. The Robust SOFM neural network is utilized to recognize the complementary features. These features are integrated using a criteria based on activity level. Fused of both MR, CT images are made from a fused feature set. Finally the fused image is attained by executing Inverse Dual Tree Complex Wavelet Transform (IDTCWT). The experimental results show that the proposed algorithm can significantly outperform image fusion technique and has a better Peak Signal to Noise Ratio (PSNR) value as compared with other transforms, DWT, FDCT, NSCT, and DTCWT.