Hybrid Contourlet transform and Coupled Neural Network based Image Fusion
Keywords:
on-subsampled Contourlet Transform (NSCT), Neural Network (NN), PSNR, MSEAbstract
This research work proposes an improved fusion technique for medical images using Non-subsampled Contourlet Transform (NSCT) and Neural Network (NN). The proposed approach is based on two processes, namely, image enhancement and image fusion to obtain more information on the fused image. The construction proposed in this paper is based on a non-subsampled pyramid structure and non-subsampled directional filter banks. The result is a flexible multiscale, multi-direction, and shift invariant image decomposition that can be efficiently implemented via the à trous algorithm. At the core of the proposed scheme is the non-separable two-channel non-subsampled filter bank (NSFB). The low resolution Positron Emission Tomography (PET) image is enhanced using Lagrange interpolation technique and then combined with the Magnetic Resonance Image (MRI) using proposed image fusion. By adopting the proposed interpolation the edge preservation is achieved, the spectral and spatial qualities are improved. Experimental results show that the application of proposed fusion has higher Peak Signal to Noise Ratio (PSNR) values with good visual perception. Comparing with other fusion methods, the proposed method has higher average gradient lower discrepancy and less Mean Square Error (MSE). Therefore the method proposed exhibits better image quality and proved to be advantageous.
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