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Multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines

Published:August 27, 2012DOI:https://doi.org/10.1016/j.jns.2012.07.064

      Abstract

      White matter changes (WMC) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMC labor intensive and prone to subjective bias. We present a fast, fully automated method for WMC segmentation using a cascade of reduced support vector machines (SVMs) with active learning. Data of 102 subjects was used in this study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC from each subject were used for the image analysis. The segmentation framework comprises pre-processing, classification (training and core segmentation) and post-processing. After pre-processing, the model was trained on two subjects and tested on the remaining 100 subjects. The effectiveness and robustness of the classification was assessed using the receiver operating curve technique. The cascade of SVMs segmentation framework outputted accurate results with high sensitivity (90%) and specificity (99.5%) values, with the manually outlined WMC as reference. An algorithm for the segmentation of WMC is proposed. This is a completely competitive and fast automatic segmentation framework, capable of using different input sequences, without changes or restrictions of the image analysis algorithm.

      Keywords

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      References

        • Breteler M.M.
        • Van Swieten J.C.
        • Bots M.L.
        • Grobbee D.E.
        • Claus J.J.
        • Van Den Hout J.H.
        • et al.
        Cerebral white matter lesions, vascular risk factors, and cognitive function in a population-based study.
        Neurology. 1994; 44: 1246-1252
        • Launer L.J.
        • Berger K.
        • Breteler M.M.
        • Dufouil C.
        • Fuhrer R.
        • Giampaoli S.
        • et al.
        Regional variability in the prevalence of cerebral white matter lesions: an MRI study in 9 European countries (CASCADE).
        Neuroepidemiology. 2006; 26: 23-29
        • Debette S.
        • Markus H.S.
        The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis.
        BMJ. 2010; 341
        • Fazekas F.
        • Chawluk J.B.
        • Alavi A.
        • Hurtig H.I.
        • Zimmerman R.A.
        MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging.
        AJR Am J Roentgenol. 1987; 149: 351
        • Wahlund L.O.
        • Barkhof F.
        • Fazekas F.
        • Bronge L.
        • Augustin M.
        • Sjögren M.
        • et al.
        A new rating scale for age-related white matter changes applicable to MRI and CT.
        Stroke. 2001; 32: 1318-1322
        • Prins N.
        • Van Straaten E.
        • Van Dijk E.
        • Simoni M.
        • Van Schijndel R.
        • Vrooman H.
        • et al.
        Measuring progression of cerebral white matter lesions on MRI.
        Neurology. 2004; 62: 1533-1539
        • van Straaten E.
        • Fazekas F.
        • Rostrup E.
        • Scheltens P.
        • Schmidt R.
        • Pantoni L.
        • et al.
        Impact of white matter hyperintensities scoring method on correlations with clinical data.
        Stroke. 2006; 37: 836-840
        • Anbeek P.
        • Vincken K.
        • van Osch M.
        • Bisschops R.
        • van der Grond J.
        Probabilistic segmentation of white matter lesions in MR imaging.
        Neuroimage. 2004; 21: 1037-1044
        • Mohamed F.B.
        • Vinitski S.
        • Gonzalez C.F.
        • Faro S.H.
        • Lublin F.A.
        • Knobler R.
        • et al.
        Increased differentiation of intracranial white matter lesions by multispectral 3D-tissue segmentation: preliminary results.
        Magn Reson Imaging. 2001; 19: 207-218
        • Wu Y.
        • Warfield S.K.
        • Tan I.L.
        • Wells III, W.M.
        • Meier D.S.
        • van Schijndel R.A.
        • et al.
        Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI.
        Neuroimage. 2006; 32: 1205-1215
        • Johnston B.
        • Atkins M.S.
        • Mackiewich B.
        • Anderson M.
        Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI.
        Med Imaging. 1996; 15: 154-169
        • Khayati R.
        • Vafadust M.
        • Towhidkhah F.
        • Nabavi M.
        Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model.
        Comput Biol Med. 2008; 38: 379-390
        • Van Leemput K.
        • Maes F.
        • Vandermeulen D.
        • Colchester A.
        • Suetens P.
        Automated segmentation of multiple sclerosis lesions by model outlier detection.
        Med Imaging. 2001; 20: 677-688
        • García-Lorenzo D.
        • Prima S.
        • Morrissey S.
        • Barillot C.
        A robust expectation–maximization algorithm for multiple sclerosis lesion segmentation.
        in: In MICCAI workshop: 3D segmentation in the clinic: a grand challenge II, MS lesion segmentation. États-Unis, New York2008: 277
        • Lao Z.
        • Shen D.
        • Liu D.
        • Jawad A.F.
        • Melhem E.R.
        • Launer L.J.
        • et al.
        Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine.
        Acad Radiol. 2008; 15: 300-313
        • Quddus A.
        • Fieguth P.
        • Basir O.
        Adaboost and support vector machines for white matter lesion segmentation in MR images.
        in: IEEE-EMBS 2005. 2005: 463-466
        • Alves G.
        • Brønnick K.
        • Aarsland D.
        • Blennow K.
        • Zetterberg H.
        • Ballard C.
        • et al.
        CSF amyloid-β and tau proteins, and cognitive performance, in early and untreated Parkinson's disease: the Norwegian ParkWest study.
        J Neurol Neurosurg Psychiatry. 2010; 81: 1080-1086
        • Aarsland D.
        • Rongve A.
        • Piepenstock Nore S.
        • Skogseth R.
        • Skulstad S.
        • Ehrt U.
        • et al.
        Frequency and case identification of dementia with Lewy bodies using the revised consensus criteria.
        Dement Geriatr Cogn Disord. 2008; 26: 445-452
        • Firbank M.
        • Lloyd A.
        • Ferrier N.
        • O'Brien J.
        A volumetric study of MRI signal hyperintensities in late-life depression.
        Am J Geriatr Psychiatry. 2004; 12: 606-612
        • Jenkinson M.
        • Smith S.
        A global optimisation method for robust affine registration of brain images.
        Med Image Anal. 2001; 5: 143-156
        • Smith S.M.
        Fast robust automated brain extraction.
        Hum Brain Mapp. 2002; 17: 143-155
        • Sled J.G.
        • Zijdenbos A.P.
        • Evans A.C.
        A nonparametric method for automatic correction of intensity nonuniformity in MRI data.
        Med Imaging. 1998; 17: 87-97
        • Chawla N.
        • Bowyer K.
        • Hall L.
        • Kegelmeyer W.
        SMOTE: synthetic minority over-sampling technique.
        J Artif Intell Res. 2002; 16: 321-357
        • Bordes A.
        • Ertekin S.
        • Weston J.
        • Bottou L.
        Fast kernel classifiers with online and active learning.
        JMLR. 2005; 6: 1579-1619
        • Romdhani S.
        • Torr P.
        • Scholkopf B.
        • Blake A.
        Computationally efficient face detection.
        in: Proc. Eighth IEEE Int. Conf. Computer Vision ICCV 2001. 2001: 695-700
        • Scholkopf B.
        • Mika S.
        • Burges C.
        • Knirsch P.
        • Muller K.
        • Ratsch G.
        • et al.
        Input space versus feature space in kernel-based methods.
        IEEE T Neural Networ. 1999; 10: 1000-1017
        • Klöppel S.
        • Abdulkadir A.
        • Hadjidemetriou S.
        • Issleib S.
        • Frings L.
        • Thanh T.
        • et al.
        A comparison of different automated methods for the detection of white matter lesions in MRI data.
        Neuroimage. 2011; 57: 416-422
        • 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: 311-318