OJMI  Vol.1 No.2 , December 2011
Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs
ABSTRACT
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI). The technique uses textural features to describe the blocks of each MRI slice along with position and neighborhood features. A trained support vector machine (SVM) is used to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions based on mainly the textural features with aid of the other features. The MRI slice blocks’ classification is used to provide an initial segmentation. A comprehensive post processing module is then utilized to refine and improve the quality of the initial segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated process without the need to manually define regions of interest (ROIs). In addition, the post processing module is generic enough to be applied to the results of any other MS segmentation technique to improve the segmentation quality. This technique is evaluated using ten real MRI data-sets with 10% used in the training of the textural-based SVM. The average results for the performance evaluation of the presented technique were 0.79 for dice similarity, 0.68 for sensitivity and 0.9 for the percentage of the detected lesion load. These results indicate that the proposed method would be useful in clinical practice for the detection of MS lesions from MRI.

Cite this paper
nullB. Abdullah, A. Younis, P. Pattany and E. Saraf-Lavi, "Textural Based SVM for MS Lesion Segmentation in FLAIR MRIs," Open Journal of Medical Imaging, Vol. 1 No. 2, 2011, pp. 26-42. doi: 10.4236/ojmi.2011.12005.
References
[1]   J. Zhang, L. Z. Tong, L. Wang and N. Li, “Texture Analysis of Multiple Sclerosis: A Comparative Study,” Magnetic Resonance Imaging, Vol. 26, No. 8, 2008, pp. 1160-1166. doi:10.1016/j.mri.2008.01.016

[2]   C. H. Polman, et al., “Diagnostic Criteria for Multiple Sclerosis: 2005 Revisions to the ‘McDonald Criteria’,” Annals of Neurology, Vol. 58, No. 6, 2005, pp. 840-856. doi:10.1002/ana.20703

[3]   D. H. Miller, R. I. Grossman, S. C. Reingold and F. Mc-Farlan, “The Role of Magnetic Resonance Techniques in Understanding and Managing Multiple Sclerosis,” Brain, Vol. 121, No. 1, 1998, pp. 3-24. doi:10.1093/brain/121.1.3

[4]   D. Yamamoto, et al., “Computer-Aided Detection of Multiple Sclerosis Lesions in Brain Magnetic Resonance Images: False Positive Reduction Scheme Consisted of Rule-Based, Level Set Method, and Support Vector Machine,” Computerized Medical Imaging and Graphics, Vol. 34, No. 5, 2010, pp. 404-413. doi:10.1016/j.compmedimag.2010.02.001

[5]   E. Geremia, et al., “Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images,” Medical Image Computing and Computer-Assisted Intervention, Vol. 6361, 2010, pp. 111-118.

[6]   C. J. Wallace, T. P. Seland and T. C. Fong, “Multiple Sclerosis: The Impact of MR Imaging,” American Journal of Roentgenology, Vol. 158, 1992, pp. 849-857.

[7]   S. Wiebe, et al., “Serial Cranial and Spinal Cord Magnetic Resonance Imaging in Multiple Sclerosis,” Annals of Neurology, Vol. 32, No. 5, 1992, pp. 643-650. doi:10.1002/ana.410320507

[8]   L. Truyen, “Magnetic Resonance Imaging in Multiple Sclerosis: A Review,” Acta Neurologica Belgica, Vol. 94, 1994, pp. 98-102.

[9]   F. Fazekas, et al., “The Contribution of Magnetic Resonance Imaging to the Diagnosis of Multiple Sclerosis,” Neurology, Vol. 53, 1999, pp. 448-456.

[10]   P. Anbeek, K. L. Vincken and M. A. Viergever, “Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification,” MIDAS Journal, 2008. http://hdl.handle.net/10380/1448

[11]   F. Rousseau, F. Blanc, J. de Seze, L. Rumbach and J. Armspach, “An a Contrario Approach for Outliers Segmentation: Application to Multiple Sclerosis in MRI,” 5th IEEE International Symposium, Paris, 2008, pp. 9-12.

[12]   B. Johnston, M. S. Atkins, B. Mackiewich and M. Anderson, “Segmentation of Multiple Sclerosis Lesions in Intensity Corrected Multispectral MRI,” IEEE Transactions on Medical Imaging, Vol. 15, No. 2, 1996, pp. 154-169. doi:10.1109/42.491417

[13]   A. O. Boudraa, et al., “Automated Segmentation of Mul-tiple Sclerosis Lesions in Multispectral MR Imaging Using Fuzzy Clustering,” Computers in Biology and Medicine, Vol. 30, No. 1,2000, pp. 23-40.

[14]   K. V. Leemput, F. Maes, D. Vandermeulen, A. Colchester and P. Suetens, “Automated Segmentation of Multiple Sclerosis Lesions by Model Outlier Detection,” IEEE Transactions on Medical Imaging, Vol. 20, No. 8, 2001, pp. 677-688. doi:10.1109/42.938237

[15]   A. P. Zijdenbos, R. Forghani and A. C. Evans, “Automatic ‘Pipeline’ Analysis of 3-D MRI Data for Clinical Trials: Application to Multiple Sclerosis,” IEEE Transactions on Medical Imaging, Vol. 21, No. 10, 2002, pp. 1280-1291. doi:10.1109/TMI.2002.806283

[16]   F. Kruggel, S. P. Joseph and H.-J. Gertz, “Texture-Based Segmentation of Diffuse Lesions of the Brain’s White Matter,” NeuroImage, Vol. 39, No. 3, 2008, pp. 987-996. doi:10.1016/j.neuroimage.2007.09.058

[17]   W. F. Liu, X. X. Zhou, G. L. Jiang and L. Z. Tong, “Texture Analysis of MRI in Patients with Multiple Sclerosis Based on the Gray-Level Difference Statistics,” Education Technology and Computer Science, Vol. 3, 2009, pp. 771-774.

[18]   A. Pozdnukhov and M. Kanevski, “Monitoring Network Optimisation for Spatial Data Classification Using Support Vector Machines,” International Journal of Environment and Pollution, Vol. 28, No. 3-4, 2006, pp. 465-484.

[19]   M. Kanevski, M. Maignan and A. Pozdnukhov, “Active Learning of Environmental Data Using Support Vector Machines,” Conference of the International Association for Mathematical Geology, Toronto, 21-26 August 2005.

[20]   R. R. Edelman, J. R. Hesselink, M. B. Zlatkin and J. V. Crues, “Clinical Magnetic Resonance Imaging,” 3rd Edition, Elsevier, New York, 2006.

[21]   R. Khayati, M. Vafadust, F. Towhidkhah and S. M. Nabavi, “Fully Automatic Segmentation of Multiple Sclerosis Lesions in Brain MR FLAIR Images Using Adaptive Mixtures Method and Markov Random Field Model,” Computers in Biology and Medicine, Vol. 38, 2008, pp. 379-390.

[22]   W. I. McDonald, et al., “Recommendation Diagnostic Criteria for Multiple Sclerosis: Guidelines from the International Panel on the Diagnosis of Multiple Sclerosis,” Annals of Neurology, Vol. 50, 2001, pp. 121-127.

[23]   A. Younis, M. Ibrahim, M. Kabuka and N. John, “An Artificial Immune-Activated Neural Network Applied to Brain 3D MRI Segmentation,” Journal of Digital Imaging, Vol. 21, Suppl. 1, 2008, pp. S69-S88.

[24]   J. Rexilius, H. K. Hahn, H. Bourquain and H.-O. Peitgen, “Ground Truth in MS Lesion Volumetry—A Phantom Study,” Lecture Notes in Computer Science, Vol. 2879, 2003, pp. 546-553.

[25]   C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for Support Vector Machines,” ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, 2011, pp. 1-27.

[26]   A. K. Jain, R. P. W. Duin and J. Mao, “Statistical Pattern Recognition: Review,” IEEE Transactions Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 4-37. doi:10.1109/34.824819

[27]   D. Garcia-Lorenzo, L. Lecoeur, D. Arnold and D. L. Collins, “Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts,” Medical Image Computing and Computer Assisted Intervention, London, 20-24 September 2009, pp. 584-591.

[28]   D. Goldberg-Zimring, H. Azhari, S. Miron and A. Achiron, “3-D Surface Reconstruction of Multiple Sclerosis Lesions Using Spherical Harmonics,” Magnetic Resonance Imaging, Vol. 46, 2001, pp. 756-766.

[29]   Z. R. Chi, H. Yan and T. Pham, “Fuzzy Algorithms: With Application to Image Processing and Pattern Recognition,” World Scientific Publishing Co. Pte. Ltd, Singapore, 1996.

[30]   J. Lecoeur, et al., “Multiple Sclerosis Lesions Segmentation using Spectral Gradient and Graph Cuts,” Proceedings of MICCAI Workshop on Medical Image Analysis on Multiple Sclerosis, New York, September 2008.

 
 
Top