MRI images denoising based in Dual-Tree complex Wavelet and Bayesian MAP Estimator

MRI images are often subject to noise (artifacts). We evaluated the performance of DT-CWT combined to Bayesian MAP Estimator to restore those images. We chose the images from two of four sequences commonly used in coronal and axial MRI with and without contrast agent. The image with contrast agent was used as a reference, and the image without where we added artificially noise was subjected to de-noising by forward Dual-Tree Wavelet Transform (DT-CWT) combined to Bayesian MAP estimator. A test was submitted to radiologists for an assessment of the de-noised images to compare the proposed algorithm to other effective techniques from the recent literature using MRI images. Our approach contributed effectively to the MRI images de-noising, with better results. In general, wavelets and Bayesian estimator contributed effectively to the denoising process and to other image processing methods, but ranging from classic to complex wavelet transforms, the results gradually improved.

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