Bias Correction for Magnetic Resonance Images via Joint Entropy Regularization
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China | School of Information Technologies, The University of Sydney, NSW 2006, Australia | School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China | Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, China | SAIIP, School of Computer Sciences, Northwestern Polytechnical University, Xi'an 710072, China
Note: [] This work was supported in part by the ARC grants and in part by the China Scholarship Council under Grant 2011623084.
Abstract: Due to the imperfections of the radio frequency (RF) coil or object-dependent electrodynamic interactions, magnetic resonance (MR) images often suffer from a smooth and biologically meaningless bias field, which causes severe troubles for subsequent processing and quantitative analysis. To effectively restore the original signal, this paper simultaneously exploits the spatial and gradient features of the corrupted MR images for bias correction via the joint entropy regularization. With both isotropic and anisotropic total variation (TV) considered, two nonparametric bias correction algorithms have been proposed, namely IsoTVBiasC and AniTVBiasC. These two methods have been applied to simulated images under various noise levels and bias field corruption and also tested on real MR data. The test results show that the proposed two methods can effectively remove the bias field and also present comparable performance compared to the state-of-the-art methods.
Keywords: Bias correction, magnetic resonance (MR) images, joint entropy, total variation (TV)
