A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images

It is very important to detect stages of multiple sclerosis (MS) lesions in order to exactly quantify involved voxels. In this paper, a novel method is proposed for automatic detection of different stages of MS lesions in the brain magnetic resonance (MR) images, in fluid attenuated inversion recovery (FLAIR) studies. In the proposed method, firstly, MS lesion voxels are segmented in FLAIR images based on adaptive mixtures method (AMM) and Markov Random Field (MRF) model. Then, signal intensity of each lesion voxel is modeled as a linear combination of signals related to the normal and also abnormal parts, in the voxel. By applying an optimal threshold, voxels with new intensities are primarily classified into two stages: previously destructed (chronic) and on going destruction (acute) lesions. Finally, the acute lesions, according to their activities, are classified, by another optimal threshold, into two new stages, early and recent acute. Evaluation of the proposed method was performed by manual segmentation of chronic and enhanced (early) acute lesions in gadolinium enhanced T1-weighted (Gad-E-T1-w) images by studying T1-weighted (T1-w) and T2-weighted (T2-w) images, using similarity criteria. The results showed a good correlation between the lesions segmented by the proposed method and by experts manually. Thus, the suggested method is useful to reduce the need for paramagnetic materials in contrast enhanced MR imaging which is a routine procedure for separation of acute and chronic lesions.

[1]  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.

[2]  J. Hogg.  Magnetic resonance imaging. , 1994, Journal of the Royal Naval Medical Service.

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

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

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

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

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

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

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

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

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

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

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

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

[16]  Farzad Towhidkhah,et al.  Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and markov random field model , 2008, Comput. Biol. Medicine.

[17]  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.

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

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