Document Type : Original Research
Authors
1 MSc, Department of Biomedical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2 PhD, Department of Biomedical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
3 PhD, Department of Neurology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
4 PhD Student, Department of Biomedical Physics and Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Abstract
Background: Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease.
Material and Methods: In this analytical study, two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients’ FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures.
Results: To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist’s work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively.
Conclusion: An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.
Keywords
- Browne P, Chandraratna D, Angood C, Tremlett H, Baker C, Taylor BV, et al. Atlas of Multiple Sclerosis 2013: A growing global problem with widespread inequity. Neurology. 2014;83:1022-4. doi: 10.1212/WNL.0000000000000768. PubMed PMID: 25200713; PubMed Central PMCID: PMC4162299.
- Multiple Sclerosis (MS) Overview [Internet]. What is Multiple Sclerosis. [Accessed: 21-Dec-2017]. Available from: http://www.msunites.com/what-is-multiple-sclerosis-ms
- Filippi M, Horsfield MA, Tofts PS, Barkhof F, Thompson AJ, Miller DH. Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis. Brain. 1995;118 ( Pt 6):1601-12. doi: 10.1093/brain/118.6.1601.PubMed PMID: 8595489.
- Polman CH, Reingold SC, Banwell B, Clanet M, Cohen JA, Filippi M, et al. Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol. 2011;69:292-302. doi: 10.1002/ana.22366. PubMed PMID: 21387374; PubMed Central PMCID: PMC3084507.
- Haller S, Pereira VM, Lalive PH, Chofflon M, Vargas MI, Lövblad KO. Magnetic resonance imaging in multiple sclerosis. Top Magn Reson Imaging. 2009;20:313-23.
- Ferrari RJ, Wei X, Zhang Y, Scott JN, Mitchell JR, editors. Segmentation of multiple sclerosis lesions using support vector machines. Medical Imaging 2003: Image Processing; 2003: International Society for Optics and Photonics. doi: 10.1117/12.481377.
- Anbeek P, Vincken KL, van Osch MJ, Bisschops RH, van der Grond J. Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal. 2004;8:205-15. doi: 10.1016/j.media.2004.06.019. PubMed PMID: 15450216.
- Wicks DA, Barker GJ, Tofts PS. Correction of intensity nonuniformity in MR images of any orientation. Magn Reson Imaging. 1993;11:183-96. doi 10.1016/0730-725x(93)90023-7.PubMed PMID: 8455430.
- Goldberg-Zimring D, Achiron A, Miron S, Faibel M, Azhari H. Automated detection and characterization of multiple sclerosis lesions in brain MR images. Magn Reson Imaging. 1998;16:311-8. doi: /10.1016/s0730-725x(97)00300-7PubMed PMID: 9621972.
- Mohamed FB, Vinitski S, Gonzalez CF, Faro SH, Lublin FA, Knobler R, et al. Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results. Magn Reson Imaging. 2001;19:207-18. doi: 10.1016/s0730-725x(01)00291-0.PubMed PMID: 11358659.
- Cabezas M, Oliver A, Roura E, Freixenet J, Vilanova JC, Ramio-Torrenta L, et al. Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding. Comput Methods Programs Biomed. 2014;115:147-61. doi: 10.1016/j.cmpb.2014.04.006. PubMed PMID: 24813718.
- Abdullah BA, Younis AA, John NM. Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs. Open Biomed Eng J. 2012;6:56-72. doi: 10.2174/1874230001206010056. PubMed PMID: 22741026; PubMed Central PMCID: PMC3382289.
- Farhan S, Fahiem MA, Tauseef H. An ensemble-of-classifiers based approach for early diagnosis of Alzheimer’s disease: classification using structural features of brain images. Comput Math Methods Med. 2014;2014:862307. doi: 10.1155/2014/862307. PubMed PMID: 25276224; PubMed Central PMCID: PMC4172935.
- Sorensen L, Nielsen M, Alzheimer’s Disease Neuroimaging I. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination. J Neurosci Methods. 2018;302:66-74. doi: 10.1016/j.jneumeth.2018.01.003. PubMed PMID: 29378218.
- Amart-stats-tools [Internet]. Lesion Challenge. [Accessed: 04-Aug-2018]. Available from: https://smart-stats-tools.org/
- Carass A, Roy S, Jog A, Cuzzocreo JL, Magrath E, Gherman A, et al. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. Neuroimage. 2017;148:77-102. doi: 10.1016/j.neuroimage.2016.12.064. PubMed PMID: 28087490; PubMed Central PMCID: PMC5344762.
- Anbeek P, Vincken KL, Van Osch MJ, Bisschops RH, Van Der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. Neuroimage. 2004;21:1037-44. doi: 10.1016/j.neuroimage.2003.10.012. PubMed PMID: 15006671.
- Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26:297-302. d0i: 10.2307/1932409.
- Jaccard P. The distribution of the flora in the alpine zone. 1. New phytologist. 1912;11:37-50. doi: 10.1111/j.1469-8137.1912.tb05611.x.
- Jacobs D. Image gradients. Class Notes for CMSC. 2005;426.
- Chen PC, Pavlidis T. Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm. Computer graphics and image processing. 1979;10:172-82. doi; 10.1016/0146-664x(79)90049-2.
- Esmael B, Arnaout A, Fruhwirth RK, Thonhauser G. A statistical feature-based approach for operations recognition in drilling time series. International Journal of Computer Information Systems and Industrial Management Applications. 2015;5:454-61.
- Lézoray O, Grady L. Image processing and analysis with graphs: theory and practice: CRC Press; 2012.
- Mudigonda NR, Rangayyan RM, Desautels JE. Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imaging. 2000;19:1032-43. doi: 10.1109/42.887618. PubMed PMID: 11131493.
- Baraldi A, Parmiggiani F. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters. IEEE Transactions on Geoscience and Remote Sensing. 1995;33:293-304. doi: 10.1109/36.377929.
- Pohjalainen J, Räsänen O, Kadioglu S. Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Computer Speech & Language. 2015;29:145-71. doi: 10.1016/j.csl.2013.11.004.
- Gonzalez RC, Woods RE. Digital image processing Third edition. Beijing: Publishing House of Electronics Industry. 2002;455.
- Jähne B. Digital Image Processing. Berlin: Springer; 2005.
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