Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and markov random field model

In this paper, an approach is proposed for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed approach, based on a Bayesian classifier, utilizes the adaptive mixtures method (AMM) and Markov random field (MRF) model to obtain and upgrade the class conditional probability density function (CCPDF) and the a priori probability of each class. To compare the performance of the proposed approach with those of previous approaches including manual segmentation, the similarity criteria of different slices related to 20 MS patients were calculated. Also, volumetric comparison of lesions volume between the fully automated segmentation and the gold standard was performed using correlation coefficient (CC). The results showed a better performance for the proposed approach, compared to those of previous works.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Angel R. Martinez,et al.  Computational Statistics Handbook with MATLAB , 2001 .

[3]  R. Kikinis,et al.  Quantitative follow‐up of patients with multiple sclerosis using MRI: Technical aspects , 1999, Journal of magnetic resonance imaging : JMRI.

[4]  A. Baraldi,et al.  Segmentation of multiple sclerosis lesions in MRI by fuzzy neural networks: FLVQ and FOSART , 1998, 1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353).

[5]  Max A. Viergever,et al.  Automatic Morphology-Based Brain Segmentation (MBRASE) from MRI-T1 Data , 2000, NeuroImage.

[6]  Jeremy Michael Nett,et al.  THE STUDY OF MS USING MRI, IMAGE PROCESSING, AND VISUALIZATION , 2001 .

[7]  R. Killiany,et al.  Quantitative follow‐up of patients with multiple sclerosis using MRI: Reproducibility , 1999, Journal of magnetic resonance imaging : JMRI.

[8]  Edoardo Ardizzone,et al.  Two channels fuzzy c-means detection of multiple sclerosis lesions in multispectral MR images , 2002, Proceedings. International Conference on Image Processing.

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

[10]  Stan Z. Li.  Markov Random Field Modeling in Image Analysis , 2009, Advances in Pattern Recognition.

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

[12]  Klaus D. Tönnies,et al.  Segmentation of multiple sclerosis lesions from MR brain images using the principles of fuzzy-connectedness and artificial neuron networks , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[13]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[14]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[15]  Abdel-Ouahab Boudraa,et al.  Automated segmentation of multiple sclerosis lesions in multispectral MR imaging using fuzzy clustering , 2000, Comput. Biol. Medicine.

[16]  Koenraad Van Leemput,et al.  Automated segmentation of multiple sclerosis lesions by model outlier detection , 2001, IEEE Transactions on Medical Imaging.

[17]  D. Louis Collins,et al.  Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images , 1995, IEEE Trans. Medical Imaging.

[18]  Ying Wu,et al.  Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI , 2006, NeuroImage.

[19]  Koen L. Vincken,et al.  Probabilistic segmentation of white matter lesions in MR imaging , 2004, NeuroImage.

[20]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[21]  Johan H. C. Reiber,et al.  Fully automatic segmentation of white matter hyperintensities in MR images of the elderly , 2005, NeuroImage.

[22]  S. Reingold,et al.  Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria” , 2005, Annals of neurology.

[23]  C. W. Therrien,et al.  Decision, Estimation and Classification: An Introduction to Pattern Recognition and Related Topics , 1989 .

[24]  J. Bartko.  Measurement and reliability: statistical thinking considerations. , 1991, Schizophrenia bulletin.

[25]  A Dromigny-Badin,et al.  A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI. , 1998, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[26]  A. Dale Magoun,et al.  Decision, estimation and classification , 1989 .

[27]  Ron Kikinis,et al.  Markov random field segmentation of brain MR images , 1997, IEEE Transactions on Medical Imaging.

[28]  R. Edelman,et al.  Clinical magnetic resonance imaging , 1990 .

[29]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[30]  Supun Samarasekera,et al.  Multiple sclerosis lesion quantification using fuzzy-connectedness principles , 1997, IEEE Transactions on Medical Imaging.

[31]  Alan C. Evans,et al.  Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis , 2002, IEEE Transactions on Medical Imaging.

[32]  R I Grossman,et al.  Perspectives on multiple sclerosis. , 1998, AJNR. American journal of neuroradiology.

[33]  J. Loewenthal.  DECISION , 1969, Definitions.