An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects

Abstract In automated image processing the intensity inhomogeneity of MR images causes significant errors. In this work we analyze three algorithms with the purpose of intensity inhomogeneity correction. The well-known N3 algorithm is compared to two more recent approaches: a modified level set method, which is able to deal with intensity inhomogeneity and it is, as well, compared to an adaptation of the fuzzy c-means clustering with intensity inhomogeneity compensation techniques. We evaluate the outcomes of these three algorithms with quantitative performance measures. The measurements are done on the bias fields and on the segmented images. We consider normal brain images obtained from the Montreal Simulated Brain Database.

[1]  Weili Zheng,et al.  Evaluation of performance metrics for bias field correction in MR brain images , 2009, Journal of magnetic resonance imaging : JMRI.

[2]  László Szilágyi,et al.  Novel image processing methods based on fuzzy logic , 2008 .

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

[4]  Brian B. Avants,et al.  N4ITK: Improved N3 Bias Correction , 2010, IEEE Transactions on Medical Imaging.

[5]  D. Mumford,et al.  Optimal approximations by piecewise smooth functions and associated variational problems , 1989 .

[6]  Mohammed Yakoob Siyal,et al.  An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI , 2005, Pattern Recognit. Lett..

[7]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[8]  Bostjan Likar,et al.  A Review of Methods for Correction of Intensity Inhomogeneity in MRI , 2007, IEEE Transactions on Medical Imaging.

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

[10]  Chunming Li,et al.  A Level Set Method for Image Segmentation in the Presence of Intensity Inhomogeneities With Application to MRI , 2011, IEEE Transactions on Image Processing.

[11]  Paul M. Thompson,et al.  Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils , 2008, NeuroImage.

[12]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[13]  Emma B. Lewis,et al.  Correction of differential intensity inhomogeneity in longitudinal MR images , 2004, NeuroImage.

[14]  Michael Brady,et al.  Estimating the bias field of MR images , 1997, IEEE Transactions on Medical Imaging.

[15]  László Szilágyi,et al.  Efficient inhomogeneity compensation using fuzzy c-means clustering models , 2012, Comput. Methods Programs Biomed..