Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields

  • Alejandro Reyes Universidad Autónoma de San Luis Potosí
  • Alfonso Alba Universidad Autónoma de San Luis Potosí https://orcid.org/0000-0002-1148-0383
  • Martín O. Méndez Universidad Autónoma de San Luis Potosí
  • Edgar R. Arce-Santana Universidad Autónoma de San Luis Potosí
  • Ildefonso Rodríguez Leyva Universidad Autónoma de San Luis Potosí
Keywords: Multiple sclerosis, Lesion detection, Superpixels, GMMF, Image segmentation

Abstract

Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and progress monitoring of MS is performed by the aid of T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying results. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues and may also reduce the execution time, while the GMMF classifier is robust to noise and also computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results without requiring user assistance nor anatomical prior information.

References

Benito-León J, Morales JM, Rivera-Navarro J, Mitchell AJ. A review about the impact of multiple sclerosis on health-related quality of life. Disability and Rehabilitation. 2003;25(23):1291-1303. https://doi.org/10.1080/09638280310001608591

Manjón JV, Coupé P. volBrain: An Online MRI Brain Volumetry System. Frontiers in Neuroinformatics. 2016; 10:30. https://doi.org/10.3389/fninf.2016.00030

Smith SM. Fast robust automated brain extraction. Human Brain Mapping. 2002;17(3):143-155. https://doi.org/10.1002/hbm.10062

Mortazavi D, Kouzani AZ, Soltanian-Zadeh H. Segmentation of multiple sclerosis lesions in MR images: a review. Neuroradiology. 2012; 54(4): 299-320. https://doi.org/10.1007/s00234-011-0886-7

Garcia-Lorenzo D, Francis S, Narayanan S, Arnold DL, Collins DL. Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging. Medical Image Analysis. 2013;17(1):1-18. https://doi.org/10.1016/j.media.2012.09.004

Khayati R, Vafadust M, TowhidkhahF , Nabavi M. Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and markov random field model. Computers in Biolology and Medicine. 2008;38(3): 379-390. https://doi.org/10.1016/j.compbiomed.2007.12.005

Khayati R, Vafadust M, Towhidkhah F, Nabavi M. A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images. Computerized Medical Imaging and Graphics. 2008;32(2):124-133. https://doi.org/10.1016/j.compmedimag.2007.10.003

Vapnik VN. An overview of statistical learning theory. IEEE Transactions on Neural Networks. 1999;10(5): 988-999. https://doi.org/10.1109/72.788640

Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC. Morphometric analysis of white matter lesion in MR images: method and validation. IEEE Transactions on Medical Imaging. 1994;13(4):716-724. https://doi.org/10.1109/42.363096

de Boer R, van der Lijn F, Vrooman HA, Vernooij MW, Ikram MA, Breteler MMB, Niessen WJ. Automatic segmentation of brain tissue and white matter lesions in MRI. In 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Arlington: IEEE;2007:652-655. https://doi.org/10.1109/ISBI.2007.356936

Awad M, Chehdi K, Nasri A. Multicomponent Image Segmentation Using a Genetic Algorithm and Artificial Neural Network. IEEE Geoscience and Remote Sensing Letters. 2007; 4(4): 571-575. https://doi.org/10.1109/LGRS.2007.903064

Shiee N, Bazin P-L, Ozturk A, Reich DS, Calabresi PA, Pham DL. A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. NeuroImage. 2010;49(2):1524-1653. https://doi.org/10.1016/j.neuroimage.2009.09.005

Aït-Ali LS, Prima S, Hellier P, Carsin B, Edan G, Barillot C. STREM: A Robust Multidimensional Parametric Method to Segment MS Lesions in MRI. In Duncan JS, Gerig G (eds.). Medical Image Computing and Computer-Assisted Intervention MICCAI. Berlin: Sprinfer.2005;3749:409-416. https://doi.org/10.1007/11566465_5

Dempster AP, Laird NM, Rubin DB. Maximum Likelihood from Incomplete Data via EM Algorithm. Journal of the Royal Statistical Society. 1977;39(1):1-22. https://doi.org/10.1111/j.2517-6161.1977.tb01600.x

García-Lorenzo D, Prima S, Morrissey SP, Barillot C. A robust Expectation-Maximization algorithm for Multiple Sclerosis lesion segmentation. MICCAI Workshop: 3D Segmentation in the Clinic: A Grand Challenge II, MS lesion segmentation. 2008:1-8.

Bartko JJ. Measurement and Reliability: Statistical Thinking Considerations. Schizophrenia Bulletin. 1991;17(3):483-489. https://doi.org/10.1093/schbul/17.3.483

Powers D. Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. Journal Machine Learning Technologies. 2011;2(1):37-63.

Lao Z, Shen D, Liu D, Jawad AF, Melhem ER, Launer LJ, Bryan RN, Davatzikos C. Computer-Assisted Segmentation of White Matter Lesions in 3D MR images using Support Vector Machine. Academic Radiology. 2008;15(3):300-313. https://doi.org/10.1016/j.acra.2007.10.012

Viola P, Wells WM. Alignment by Maximization of Mutual Information. International Journal of Computer Vision. 1997;24(2):137-154. https://doi.org/10.1023/A:1007958904918

Wang XY, Wang T, Bu J. Color image segmentation using pixel wise support vector machine classification. Pattern Recognition. 2011;44(4):777-787. https://doi.org/10.1016/j.patcog.2010.08.008

Toussaint N, Souplet JC, Fillard P. MedINRIA: Medical Image Navigation and Research Tool by INRIA. In Proceedings of MICCAI Workshop on Interaction in Medical Image Analysis and Visualization. Brisbane: MICCAI. 2007;4791:1-8.

Achata R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis Machine Intelligence. 2012;34(11):2274-2282. https://doi.org/10.1109/TPAMI.2012.120

Marroquin JL, Velasco FA, Rivera M, Nakamura M. Gauss-Markov measure field models for low-level vision. IEEE Transactions on Pattern Analysis Machine Intelligence. 2001;23(4):337-348. https://doi.org/10.1109/34.917570

Cheng J, Liu J, Xu Y, Yin F, Kee-Wong DW, Tan NM, Tao D, Cheng CY, Aung T, Wong TY. Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening. IEEE Transactions on Medical Imaging. 2013;32(6):1019-1032. https://doi.org/10.1109/TMI.2013.2247770

Ren CY, Reid I. gSLIC: a real-time implementation of SLIC superpixel segmentation. Technical Report [Internet]. 201:1-6. Available from: http://www.carlyuheng.com/pdfs/gSLIC_report.pdf.

Haralick RM, Shapiro LG. Computer and Robot Vision. Boston, United States: Addison-Wesley Longman Publishing;1992:28-48p.

Cocosco CA, Kollokian V, Kwan KS, Pike GB, Evan AC. BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. NeuroImage. 1997;5:425.

García-Lorenzo D, Lecoeur J, Arnold DL, Collins DL, Barillot C. Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts. In Yang G-Z, Hawkes D, Rueckert D, Noble A, Taylor C (eds.). Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. Berlin, Heidelberg: Springer Berlin Heidelberg; 2009:584-591. https://doi.org/10.1007/978-3-642-04271-3_71

Bricq S, Collet Ch, Armspach JP. Lesions detection on 3D brain MRI using trimmed likelihood estimator and probabilistic atlas. 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Paris; IEEE. 2008:93-96. https://doi.org/10.1109/ISBI.2008.4540940

Forbes F, Doyle S, Garcia-Lorenzo D, Barillot C, Dojat M. Adaptive weigthed fusion of multiple MR sequences for brain lesion segmentation. 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. Rotterdam: IEEE. 2010:69-72. https://doi.org/10.1109/ISBI.2010.5490413

Freifeld O, Greenspan H, Goldberger J. Multiple Sclerosis Lesion Detection Using Constrained GMM and Curve Evolution. International Journal of Biomedical Imaging. 2009: 715124. https://doi.org/10.1155/2009/715124

Published
2020-10-04
How to Cite
Reyes, A., Alba Cadena, F. A., Méndez García, M. O., Arce Santana, E. R., & Rodríguez Leyva, I. (2020). Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields. Mexican Journal of Biomedical Engineering, 41(3), 40-55. Retrieved from http://www.rmib.mx/index.php/rmib/article/view/1050
Section
Research Articles