BAYESIAN ENSEMBLE LEARNING FOR MEDICAL IMAGE DENOISING

Abstract

Medical images are often affected by random noise because of both image acquisition from the medical modalities and image transmission from modalities to workspace in the main computer. Medical image denoising removes noise from the CT or MR images and it is an essential step that makes diagnosing more efficient. Many denoising algorithms have been introduced such as Non-local Means, Fields of Experts, and BM3D. In this thesis, we implement the Bayesian ensemble learning for not only natural image denoising but also medical image denoising. The Bayesian ensemble models are Non-local Means and Fields of Experts, the very successful recent algorithms. The Non-local Means presumes that the image contains an extensive amount of self-similarity. The approach of the Fields of Experts model extends traditional Markov Random Field model by learning potential functions over extended pixel neighborhoods. The two models are implemented, and image denoising is performed on both natural images and MR images. For MR images, we used two noise distributions, Gaussian and Rician. The experimental results obtained are used to compare with the single algorithm, and discuss the ensemble learning and their approaches.