Volumetric Bias Correction

This paper presents a method to suppress the bias artifact, also known as RF-inhomogeneity, in Magnetic Resonance Imaging (MRI). This artifact produces illumination variations due to magnetic field fluctuations of the device. In the latest years many works have been devoted to face this problem. In this work we present the 3D version of a new approach to bias correction, which is called Exponential Entropy Driven Homomorphic Unsharp Masking (E2D-HUM). This technique has been already presented by some of the authors for the 2D case only. The description of the whole method is detailed, and some experimental results are reported.

[1]  Guido Gerig,et al.  Compensation of Spatial Inhomogeneity in MRI Based on a Parametric Bias Estimate , 1996, VBC.

[2]  Benoit M. Dawant,et al.  Correction of intensity variations in MR images for computer-aided tissue classification , 1993, IEEE Trans. Medical Imaging.

[3]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[4]  Richard A. Robb,et al.  Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction , 1998, IEEE Transactions on Medical Imaging.

[5]  M. Stella Atkins,et al.  Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI , 1996, IEEE Trans. Medical Imaging.

[6]  John Ashburner,et al.  MRI Sensitivity Correction and Tissue Classification , 1998, NeuroImage.

[7]  Y De Deene,et al.  Fundamentals of MRI measurements for gel dosimetry , 2004 .

[8]  Richard A. Robb,et al.  Visualization in biomedical computing , 1999, Parallel Comput..

[9]  N. Thacker,et al.  A fast model independent method for automatic correction of intensity nonuniformity in MRI data , 1999, Journal of magnetic resonance imaging : JMRI.

[10]  Bostjan Likar,et al.  Retrospective correction of MR intensity inhomogeneity by information minimization , 2000, IEEE Transactions on Medical Imaging.

[11]  L. Axel,et al.  Intensity correction in surface-coil MR imaging. , 1987, AJR. American journal of roentgenology.

[12]  A. Evans,et al.  MRI simulation-based evaluation of image-processing and classification methods , 1999, IEEE Transactions on Medical Imaging.

[13]  W. Peizhuang Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek) , 1983 .

[14]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[15]  E. Ardizzone,et al.  Exponential Entropy Driven HUM on Knee MR Images , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[16]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[17]  Alan C. Evans,et al.  An Extensible MRI Simulator for Post-Processing Evaluation , 1996, VBC.

[18]  R. Gupta,et al.  Polynomial modeling and reduction of RF body coil spatial inhomogeneity in MRI , 1993, IEEE Trans. Medical Imaging.

[19]  Jeih-San Liow,et al.  Qualitative and Quantitative Evaluation of Six Algorithms for Correcting Intensity Nonuniformity Effects , 2001, NeuroImage.

[20]  Martin Styner,et al.  Parametric estimate of intensity inhomogeneities applied to MRI , 2000, IEEE Transactions on Medical Imaging.

[21]  Régis Guillemaud,et al.  Uniformity correction with homomorphic filtering on region of interest , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).