Curvempirical Transform for Multimodal Fusion of Brain Images

Abstract

Aims: Medical imaging requires special operating procedures and can cause mis-images that occur when someone is getting imaged, which can lead to inaccurate results Background: Adaptive illustration of the signal is imperative in signal processing. Empirical Wavelet Transform (EWT) is a new-fangled adaptive signal decomposition technique. Objective: Brain image fusion understands a dynamic job in medical imaging applications by assisting radiologists in detecting the variation in CT and MR images. Methods: This paper presents a fusion of filter banks of CT-MR image modalities of the Brain using the Empirical Curvelet Transform and Hybrid technique. In the hybrid technique filter banks of CT curvelet-MR little wood and CT little wood -MR curvelet were fused. The images were preprocessed using the Top Hat transform technique. The evaluation was performed based on the performance evaluation parameter. PSNR and SSIM are considered performance evaluation parameters Results: It has been observed that the results of fused filter banks using the curvelet technique show remarkable results in terms of PSNR and SSIM. The fused results show 29.10 dB PSNR and 0.819 SSIM. Conclusion: It has been observed that the fusion using only curvelet results in a 47.25% improvement in comparison with CT curvelet-MR little wood and a 42.68% improvement in comparison with CT little wood -MR curvelet.