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HosseiniPanah, S, Zamani, A, Emadi, F, HamtaeiPour, F. (1398). Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM). سامانه مدیریت نشریات علمی, 9(6), 699-710. doi: 10.31661/jbpe.v0i0.986
S HosseiniPanah; A Zamani; F Emadi; F HamtaeiPour. "Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM)". سامانه مدیریت نشریات علمی, 9, 6, 1398, 699-710. doi: 10.31661/jbpe.v0i0.986
HosseiniPanah, S, Zamani, A, Emadi, F, HamtaeiPour, F. (1398). 'Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM)', سامانه مدیریت نشریات علمی, 9(6), pp. 699-710. doi: 10.31661/jbpe.v0i0.986
HosseiniPanah, S, Zamani, A, Emadi, F, HamtaeiPour, F. Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM). سامانه مدیریت نشریات علمی, 1398; 9(6): 699-710. doi: 10.31661/jbpe.v0i0.986

Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM)

Journal of Biomedical Physics and Engineering
مقاله 14، دوره 9، شماره 6، اسفند 2019، صفحه 699-710    اصل مقاله (2.05 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.986
نویسندگان
S HosseiniPanah* 1؛ A Zamani2؛ F Emadi3؛ F HamtaeiPour4
1MSc, Department of Biomedical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
2PhD, Department of Biomedical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
3PhD, Department of Neurology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
4PhD Student, Department of Biomedical Physics and Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
چکیده
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.
کلیدواژه‌ها
Multiple Sclerosis؛ Magnetic Resonance Imaging؛ Segmentation؛ Support Vector Machine؛ Ensemble Classifier؛ Classification Lesion
مراجع
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