An Adaptive Fuzzy Segmentation Algorithm for Three-Dimensional Magnetic Resonance Images

An algorithm is proposed for the fuzzy segmentation of two and three-dimensional multispectral magnetic resonance (MR) images that have been corrupted by intensity inhomogeneities, also known as shading artifacts. The algorithm is an extension of the two-dimensional adaptive fuzzy C-means algorithm (2-D AFCM) presented in previous work by the authors. This algorithm models the intensity inhomogeneities as a gain field that causes image intensities to smoothly and slowly vary through the image space. It iteratively adapts to the intensity inhomogeneities and is completely automated. In this paper, we fully generalize 2-D AFCM to three-dimensional (3-D) multispectral images. Because of the potential size of 3-D image data, we also describe a new, faster multigrid-based algorithm for its implementation. We show using simulated MR data that 3-D AFCM yields significantly lower error rates than both the standard fuzzy C-means algorithm and several other competing methods when segmenting corrupted images. Its efficacy is further demonstrated using real 3-D scalar and multispectral MR brain images.

[1]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[2]  Anne Lohrli.  Chapman and Hall , 1985 .

[3]  M. Brandt,et al.  Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. , 1994, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[4]  W. Eric L. Grimson,et al.  Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery , 1998, MICCAI.

[5]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.

[6]  T. Foster,et al.  A review of normal tissue hydrogen NMR relaxation times and relaxation mechanisms from 1-100 MHz: dependence on tissue type, NMR frequency, temperature, species, excision, and age. , 1984, Medical physics.

[7]  William L. Briggs,et al.  A multigrid tutorial, Second Edition , 2000 .

[8]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[9]  S. Arridge,et al.  Sources of intensity nonuniformity in spin echo images at 1.5 T , 1994, Magnetic resonance in medicine.

[10]  Michael Unser,et al.  Multigrid adaptive image processing , 1995, Proceedings., International Conference on Image Processing.

[11]  Jerry L. Prince,et al.  An Automated Technique for Statistical Characterization of Brain Tissues in Magnetic Resonance Imaging , 1997, Int. J. Pattern Recognit. Artif. Intell..

[12]  Jerry L. Prince,et al.  Adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities , 1998, Medical Imaging.

[13]  Jagath C. Rajapakse,et al.  Statistical approach to segmentation of single-channel cerebral MR images , 1997, IEEE Transactions on Medical Imaging.

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

[15]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

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

[17]  J. Bezdek,et al.  Parameter estimation for finite mixture distributions , 1988 .

[18]  Z. Liang,et al.  Tissue classification and segmentation of MR images , 1993, IEEE Engineering in Medicine and Biology Magazine.

[19]  William L. Briggs,et al.  A multigrid tutorial , 1987 .

[20]  幹男 山本.  “IEEE Trans. on Medical Imaging”誌と本誌 , 1983 .