Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review

Maria Eugenia Caligiuri, Paolo Perrotta, Antonio Augimeri, Federico Rocca, Aldo Quattrone, Andrea Cherubini, Maria Eugenia Caligiuri, Paolo Perrotta, Antonio Augimeri, Federico Rocca, Aldo Quattrone, Andrea Cherubini

Abstract

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.

Figures

Fig. 1
Fig. 1
Graphical scheme of FLAIR-histoseg method. Top panels: two different axial slices of a FLAIR image and corresponding results of the segmentation. Bottom panel: histogram of the FLAIR image, intensities (arbitrary units) on the abscissa, number of voxels on the ordinate. For each FLAIR image, the thresholds used to segment the histogram in the three domains were defined by regression equations (for details see Jack et al. 2001); normal brain voxels are green; WMH voxels are red; CSF voxels are blue
Fig. 2
Fig. 2
a: signal-to-probability maps of subject with WMH. The GM probability is shown in dark gray and the WM probability is shown in light gray. WM probability values are multiplied by −1 for display purposes. Voxels classified as WMH are shown in black. b: after removing WMH voxels, the signal-to-probability maps of the patient are comparable to those of a normal brain (both GM and WM tissues are no longer contaminated by the abnormalities). From Seghier et al. (2008)
Fig. 3
Fig. 3
The WHASA method. Top panel shows the computation of the contrast parameter used for non-linear diffusion. Bottom panel illustrates the segmentation of the FLAIR image using non-linear diffusion and watershed. The third row shows a 3D visualization of the enlarged image part, where color and height indicate intensity values. Reproduced from Samaille et al. (2012)

References

    1. Admiraal-Behloul F, van den Heuvel DMJ, Olofsen H, van Osch MJP, van der Grond J, van Buchem M, Reiber JHC. Fully automatic segmentation of white matter hyperintensities in MR images of the elderly. NeuroImage. 2005;28(3):607–17. doi: 10.1016/j.neuroimage.2005.06.061.
    1. Anbeek P, Vincken KL, van Osch MJP, Bisschops RHC, van der Grond J. Probabilistic segmentation of white matter lesions in MR imaging. NeuroImage. 2004;21(3):1037–44. doi: 10.1016/j.neuroimage.2003.10.012.
    1. Anitha, M., Selvy, P. T., & Palanisamy, V. (2012). WML detection of brain images using fuzzy and possibilistic approach in feature space, WSEAS TRANSACTIONS on COMPUTERS, E-ISSN, 2224–2872.
    1. Bastianello, S., Bozzao, A., Paolillo, A., Giugni, E., Gasperini, C., Koudriavtseva, T., … Bozzao, L. (1997). Fast spin-echo and fast fluid-attenuated inversion-recovery versus conventional spin-echo sequences for MR quantification of multiple sclerosis lesions. American Journal of Neuroradiology, 18 (4), 699–704.
    1. Bastos Leite AJ, van Straaten EC, Scheltens P, Lycklama G, Barkhof F. Thalamic lesions in vascular dementia: low sensitivity of fluid-attenuated inversion recovery (FLAIR) imaging. Stroke. 2004;35(2):415–9. doi: 10.1161/01.STR.0000109226.67085.5A.
    1. Beare R, Srikanth V, Chen J, Phan TG, Stapleton J, Lipshut R, Reutens D. Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities. NeuroImage. 2009;47(1):199–203. doi: 10.1016/j.neuroimage.2009.03.055.
    1. Brickman, A. M., Sneed, J. R., Provenzano, F. a, Garcon, E., Johnert, L., Muraskin, J., … Roose, S. P. (2011). Quantitative approaches for assessment of white matter hyperintensities in elderly populations. Psychiatry Research, 193 (2), 101–6. doi:10.1016/j.pscychresns.2011.03.007.
    1. Cao, J. (1999). The size of the connected components of excursion sets of χ 2, t and F fields. Advances in Applied Probability, 579–595.
    1. Dade, L. A., Gao, F. Q., Kovacevic, N., Roy, P., Rockel, C., O’Toole, C. M., … Black, S. E. (2004). Semiautomatic brain region extraction: a method of parcellating brain regions from structural magnetic resonance images. NeuroImage, 22 (4), 1492–502. doi:10.1016/j.neuroimage.2004.03.023.
    1. de Boer, R., Vrooman, H. a, van der Lijn, F., Vernooij, M. W., Ikram, M. A., van der Lugt, A., … Niessen, W. J. (2009). White matter lesion extension to automatic brain tissue segmentation on MRI. NeuroImage, 45 (4), 1151–61. doi:10.1016/j.neuroimage.2009.01.011.
    1. Debette S, Markus HS. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666. doi: 10.1136/bmj.c3666.
    1. DeCarli, C., Murphy, D. G., Tranh, M., Grady, C. L., Haxby, J. V, Gillette, J. a, … Rapoport, S. I. (1995). The effect of white matter hyperintensity volume on brain structure, cognitive performance, and cerebral metabolism of glucose in 51 healthy adults. Neurology, 45 (11), 2077–84.
    1. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302. doi: 10.2307/1932409.
    1. Duda RO, Hart PE, Stork DG. Pattern classification. New York: Wiley; 1973.
    1. Dyrby, T. B., Rostrup, E., Baaré, W. F. C., van Straaten, E. C. W., Barkhof, F., Vrenken, H., … Waldemar, G. (2008). Segmentation of age-related white matter changes in a clinical multi-center study. NeuroImage, 41 (2), 335–45. doi:10.1016/j.neuroimage.2008.02.024.
    1. Ebihara M, Mahara H, Sakurai T, Nomura A, Miike H. Image processing by a discrete reaction–diffusion system. Proceeding of Visualization, Imaging, and Image Processing. 2003;396:145–150.
    1. Filippi M, Rovaris M, Campi A, Pereira C, Comi G. Semi-automated thresholding technique for measuring lesion volumes in multiple sclerosis: effects of the change of the threshold on the computed lesion loads. Acta Neurologica Scandinavica. 1996;93(1):30–4. doi: 10.1111/j.1600-0404.1996.tb00166.x.
    1. Filippi, M., Baratti, C., Yousry, T., Horsfield, M. A., Mammi, S., Becker, C., … & Comi, G. (1996b). Quantitative assessment of MRI lesion load in multiple sclerosis A comparison of conventional spin-echo with fast fluid attenuated inversion recovery. Brain, 119 (4), 1349–1355.
    1. Filippi, M., Rocca, M. A., Gasperini, C., Sormani, M. P., Bastianello, S., Horsfield, M. A., … Comi, G. (1999). Interscanner variation in brain MR lesion load measurements in multiple sclerosis using conventional spin-echo, rapid relaxation-enhanced, and fast-FLAIR sequences. American Journal of Neuroradiology, 20 (1), 133–7.
    1. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., … Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33 (3), 341–55.
    1. Freund Y, Schapire R, Abe N. A short introduction to boosting. Journal-Japanese Society For Artificial Intelligence. 1999;14(771–780):1612.
    1. Gao FQ, Swartz RH, Scheltens P, et al. Complexity of MRI white matter hyperintensity assessments in relation to cognition in aging and dementia from the Sunnybrook Dementia Study. Journal of Alzheimer’s Disease. 2011;26(Suppl 3):379–88.
    1. García-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. doi: 10.1016/j.media.2012.09.004.
    1. Gawne-Cain ML, O’Riordan JI, Coles A, Newell B, Thompson AJ, Miller DH. MRI lesion volume measurement in multiple sclerosis and its correlation with disability: a comparison of fast fluid attenuated inversion recovery (fFLAIR) and spin echo sequences. Journal of Neurology Neurosurgery and Psychiatry. 1998;64(2):197–203. doi: 10.1136/jnnp.64.2.197.
    1. Gibson E, Gao F, Black SE, Lobaugh NJ. Automatic segmentation of white matter hyperintensities in the elderly using FLAIR images at 3T. Journal of Magnetic Resonance Imaging. 2010;31(6):1311–22. doi: 10.1002/jmri.22004.
    1. Goldberg-Zimring D, Achiron A, Miron S, Faibel M, Azhari H. Automated detection and characterization of multiple sclerosis lesions in brain MR images. Magnetic Resonance Imaging. 1998;16(3):311–8. doi: 10.1016/S0730-725X(97)00300-7.
    1. Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing.
    1. Grimaud, J., Lai, M., Thorpe, J., Adeleine, P., Wang, L., Barker, G. J., … Miller, D. H. (1996). Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. Magnetic resonance imaging, 14 (5), 495–505.
    1. Gurol, M. E., Irizarry, M. C., Smith, E. E., Raju, S., Diaz-Arrastia, R., Bottiglieri, T., … Greenberg, S. M. (2006). Plasma beta-amyloid and white matter lesions in AD, MCI, and cerebral amyloid angiopathy. Neurology, 66 (1), 23–9. doi:10.1212/01.wnl.0000191403.95453.6a.
    1. Haller, S., Kövari, E., Herrmann, F. R., Cuvinciuc, V., Tomm, A. M., Zulian, G. B., … & Bouras, C. (2013). Do brain T2/FLAIR white matter hyperintensities correspond to myelin loss in normal aging? A radiologic-neuropathologic correlation study. Acta Neuropathologica Commun, 1, 14.
    1. Hearst MA, Dumais ST, Osman E, Platt J, Scholkopf B. Support vector machines. Intelligent Systems and their Applications IEEE. 1998;13(4):18–28. doi: 10.1109/5254.708428.
    1. Held K, Kops ER, Krause BJ, Wells WMI, Kikinis IIR, Muller-Gartner HW. Markov random field segmentation of brain MR images. Medical Imaging, IEEE Transactions on. 1997;16(6):878–886. doi: 10.1109/42.650883.
    1. Herskovits EH, Bryan RN, Yang F. Automated Bayesian segmentation of microvascular white-matter lesions in the ACCORD-MIND study. Advances in Medical Sciences. 2008;53(2):182–90.
    1. Itti L, Chang L, Ernst T. Segmentation of progressive multifocal leukoencephalopathy lesions in fluid-attenuated inversion recovery magnetic resonance imaging. Journal of Neuroimaging. 2001;11(4):412–417. doi: 10.1111/j.1552-6569.2001.tb00071.x.
    1. Jack, C. R., Brien, P. C. O., Rettman, D. W., Shiung, M. M., Xu, Y., Muthupillai, R., … Erickson, B. J. (2001). FLAIR Histogram Segmentation for Measurement of Leukoaraiosis Volume. Journal of Magnetic Resonance Imaging, 14 (6), 668–676. doi:10.1002/jmri.10011.
    1. Jeon, S., Yoon, U., Park, J. S., Seo, S. W., Kim, J. H., Kim, S. T., … & Lee, J. M. (2011). Fully automated pipeline for quantification and localization of white matter hyperintensity in brain magnetic resonance image. International Journal of Imaging Systems and Technology, 21 (2), 193–200. doi:10.1002/ima.20277.
    1. Ji S, Ye C, Li F, Sun W, Zhang J, Huang Y, Fang J. Automatic segmentation of white matter hyperintensities by an extended FitzHugh & Nagumo reaction diffusion model. Journal of Magnetic Resonance Imaging. 2013;37(2):343–50. doi: 10.1002/jmri.23836.
    1. Kawata, Y., Arimura, H., Yamashita, Y., Magome, T., Ohki, M., Toyofuku, F., … & Tsuchiya, K. (2010). Computer-aided evaluation method of white matter hyperintensities related to subcortical vascular dementia based on magnetic resonance imaging. Computerized Medical Imaging and Graphics, 34 (5), 370–376. doi:10.1016/j.compmedimag.2009.12.014.
    1. Khademi A, Venetsanopoulos A, Moody AR. Robust white matter lesion segmentation. IEEE Transactions on Biomedical Engineering. 2012;59(3):860–871. doi: 10.1109/TBME.2011.2181167.
    1. Kim KW, MacFall JR, Payne ME. Classification of white matter lesions on magnetic resonance imaging in elderly persons. Biological Psychiatry. 2008;64(4):273–80. doi: 10.1016/j.biopsych.2008.03.024.
    1. Klöppel, S., Abdulkadir, A., Hadjidemetriou, S., Issleib, S., Frings, L., Thanh, T. N., … Ronneberger, O. (2011). A comparison of different automated methods for the detection of white matter lesions in MRI data. NeuroImage, 57 (2), 416–22. doi:10.1016/j.neuroimage.2011.04.053.
    1. Kovacevic N, Lobaugh NJ, Bronskill MJ, Levine B, Feinstein A, Black SE. A robust method for extraction and automatic segmentation of brain images. NeuroImage. 2002;17(3):1087–100. doi: 10.1006/nimg.2002.1221.
    1. Kruggel F, Paul JS, Gertz H-J. Texture-based segmentation of diffuse lesions of the brain’s white matter. NeuroImage. 2008;39(3):987–96. doi: 10.1016/j.neuroimage.2007.09.058.
    1. Lao, Z., Shen, D., Liu, D., Jawad, A. F., Melhem, E. R., Launer, L. J., … Davatzikos, C. (2008). Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine. Academic Radiology, 15 (3), 300–13. doi:10.1016/j.acra.2007.10.012.
    1. Lin Y. Support vector machines and the Bayes rule in classification. Data Mining and Knowledge Discovery. 2002;6(3):259–275. doi: 10.1023/A:1015469627679.
    1. Lladó, X., Oliver, A., Cabezas, M., Freixenet, J., Vilanova, J. C., Quiles, A., … & Rovira, À. (2012). Segmentation of multiple sclerosis lesions in brain MRI: a review of automated approaches. Information Sciences, 186 (1), 164–185. doi:10.1016/j.ins.2011.10.011.
    1. Maillard, P., Delcroix, N., Crivello, F., Dufouil, C., Gicquel, S., Joliot, M., … Mazoyer, B. (2008). An automated procedure for the assessment of white matter hyperintensities by multispectral (T1, T2, PD) MRI and an evaluation of its between-centre reproducibility based on two large community databases. Neuroradiology, 50 (1), 31–42. doi:10.1007/s00234-007-0312-3.
    1. Maldjian, J., Whitlow, C. T., Saha, B. N., Kota, G., Vandergriff, C., Davenport, E. M., … Bowden, D. W. (2013). Automated white matter total lesion volume segmentation in diabetes. American Journal of Neuroradiology, 34 (12), 2265–70. doi:10.3174/ajnr.A3590.
    1. Mamdani EH, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man–machine Studies. 1975;7(1):1–13. doi: 10.1016/S0020-7373(75)80002-2.
    1. Mäntylä R, Erkinjuntti T, Salonen O, Aronen HJ, Peltonen T, Pohjasvaara T, Standertskjöld-Nordenstam CG. Variable agreement between visual rating scales for white matter hyperintensities on MRI. comparison of 13 rating scales in a poststroke cohort. Stroke. 1997;28(8):1614–23. doi: 10.1161/01.STR.28.8.1614.
    1. Matheron G. Principles of geostatistics. Economic Geology. 1963;58(8):1246–1266. doi: 10.2113/gsecongeo.58.8.1246.
    1. Ong KH, Ramachandram D, Mandava R, Shuaib IL. Automatic white matter lesion segmentation using an adaptive outlier detection method. Magnetic Resonance Imaging. 2012;30(6):807–23. doi: 10.1016/j.mri.2012.01.007.
    1. Payne ME, Fetzer DL, MacFall JR, Provenzale JM, Byrum CE, Krishnan KRR. Development of a semi-automated method for quantification of MRI gray and white matter lesions in geriatric subjects. Psychiatry Research. 2002;115(1–2):63–77. doi: 10.1016/S0925-4927(02)00009-4.
    1. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1990;12(7):629–639. doi: 10.1109/34.56205.
    1. Prins ND, van Straaten EC, van Dijk EJ, et al. Measuring progression of cerebral white matter lesions on MRI: visual ratings and volumetrics. Neurology. 2004;62(9):1533–9. doi: 10.1212/01.WNL.0000123264.40498.B6.
    1. Ramirez, J., Gibson, E., Quddus, a, Lobaugh, N. J., Feinstein, a, Levine, B., … Black, S. E. (2011). Lesion Explorer: a comprehensive segmentation and parcellation package to obtain regional volumetrics for subcortical hyperintensities and intracranial tissue. NeuroImage, 54 (2), 963–73. doi:10.1016/j.neuroimage.2010.09.013.
    1. Rangayyan RM. Biomedical image analysis. London: CRC press; 2004. pp. 583–796.
    1. Rovaris M, Comi G, Rocca MA, Cercignani M, Colombo B, Santuccio G, Filippi M. Relevance of hypointense lesions on fast fluid-attenuated inversion recovery MR images as a marker of disease severity in cases of multiple sclerosis. American Journal of Neuroradiology. 1999;20(5):813–20.
    1. Samaille, T., Fillon, L., Cuingnet, R., Jouvent, E., Chabriat, H., Dormont, D., … Chupin, M. (2012). Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation. PloS one, 7 (11), e48953. doi:10.1371/journal.pone.0048953.
    1. Scheltens, P., Erkinjunti, T., Leys, D., Wahlund, L. O., Inzitari, D., del Ser, T., … Pantoni, L. (1998). White matter changes on CT and MRI: an overview of visual rating scales. European Task Force on Age-Related White Matter Changes. European Neurology, 39 (2), 80–9.
    1. Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., … Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59 (4), 3774–83. doi:10.1016/j.neuroimage.2011.11.032.
    1. Schwarz, C., Fletcher, E., DeCarli, C., & Carmichael, O. (2009, January). Fully-automated white matter hyperintensity detection with anatomical prior knowledge and without FLAIR. In Information processing in medical imaging (pp. 239–251). Springer Berlin Heidelberg.
    1. Seghier ML, Ramlackhansingh A, Crinion J, Leff AP, Price CJ. Lesion identification using unified segmentation-normalisation models and fuzzy clustering. NeuroImage. 2008;41(4):1253–66. doi: 10.1016/j.neuroimage.2008.03.028.
    1. Sheline, Y. I., Price, J. L., Vaishnavi, S. N., Mintun, M. a, Barch, D. M., Epstein, A. a, … McKinstry, R. C. (2008). Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. The American journal of psychiatry, 165 (4), 524–32. doi:10.1176/appi.ajp.2007.07010175.
    1. Shi, L., Wang, D., Liu, S., Pu, Y., Wang, Y., Chu, W. C. W., … Wang, Y. (2013). Automated quantification of white matter lesion in magnetic resonance imaging of patients with acute infarction. Journal of Neuroscience Methods, 213 (1), 138–46. doi:10.1016/j.jneumeth.2012.12.014.
    1. Shrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin. 1979;86(2):420–428. doi: 10.1037/0033-2909.86.2.420.
    1. Simões R, Mönninghoff C, Dlugaj M, Weimar C, Wanke I, van Cappellen van Walsum A-M, Slump C. Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images. Magnetic Resonance Imaging. 2013;31(7):1182–9. doi: 10.1016/j.mri.2012.12.004.
    1. Styner, M., Lee, J., Chin, B., Chin, M., Commowick, O., Tran, H., … & Warfield, S. (2008). 3D segmentation in the clinic: A grand challenge II: MS lesion segmentation. MIDAS Journal, 2008, 1–6.
    1. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control. Systems, Man and Cybernetics IEEE Transactions on. 1985;1:116–132. doi: 10.1109/TSMC.1985.6313399.
    1. Tang H, Dillenseger JL, Bao XD, Luo LM. A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model. Computerized Medical Imaging and Graphics. 2009;33(8):644–650. doi: 10.1016/j.compmedimag.2009.07.001.
    1. Valdés Hernández MDC, Ferguson KJ, Chappell FM, Wardlaw JM. New multispectral MRI data fusion technique for white matter lesion segmentation: method and comparison with thresholding in FLAIR images. European Radiology. 2010;20(7):1684–1691. doi: 10.1007/s00330-010-1718-6.
    1. Valdés Hernández MDC, Gallacher PJ, Bastin ME, Royle NA, Maniega SM, Deary IJ, Wardlaw JM. Automatic segmentation of brain white matter and white matter lesions in normal aging: comparison of five multispectral techniques. Magnetic Resonance Imaging. 2012;30(2):222–229. doi: 10.1016/j.mri.2011.09.016.
    1. Valdés Hernández MDC, Piper RJ, Wang X, Deary IJ, Wardlaw JM. Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images: a systematic review. Journal of Magnetic Resonance Imaging. 2013;38(4):774–85. doi: 10.1002/jmri.24047.
    1. Van den Heuvel, D. M. J., ten Dam, V. H., de Craen, A. J. M., Admiraal-Behloul, F., van Es, A. C. G. M., Palm, W. M., … van Buchem, M. A. (2006). Measuring longitudinal white matter changes: comparison of a visual rating scale with a volumetric measurement. American Journal of Neuroradiology, 27 (4), 875–8.
    1. Van Straaten, E. C. W., Fazekas, F., Rostrup, E., Scheltens, P., Schmidt, R., Pantoni, L., … Barkhof, F. (2006). Impact of white matter hyperintensities scoring method on correlations with clinical data: the LADIS study. Stroke, 37 (3), 836–40. doi:10.1161/01.STR.0000202585.26325.74.
    1. Wang, Y., Catindig, J. A., Hilal, S., Soon, H. W., Ting, E., Wong, T. Y., … Qiu, A. (2012). Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts. NeuroImage, 60 (4), 2379–88. doi:10.1016/j.neuroimage.2012.02.034.
    1. Wardlaw, J. M., Smith, E. E., Biessels, G. J., Cordonnier, C., Fazekas, F., Frayne, R., … STandards for ReportIng Vascular changes on nEuroimaging (STRIVE v1) (2013). Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurology, 12 (8), 822–38. doi: 10.1016/S1474-4422 (13)70124-8.
    1. Wu, M., Rosano, C., Butters, M., Whyte, E., Nable, M., Crooks, R., … Aizenstein, H. J. (2006). A fully automated method for quantifying and localizing white matter hyperintensities on MR images. Psychiatry Research, 148 (2–3), 133–42. doi:10.1016/j.pscychresns.2006.09.003.
    1. Yang F, Shan ZY, Kruggel F. White matter lesion segmentation based on feature joint occurrence probability and χ2 random field theory from magnetic resonance (MR) images. Pattern Recognition Letters. 2010;31(9):781–790. doi: 10.1016/j.patrec.2010.01.025.
    1. Yoo, B. I., Lee, J. J., Han, J. W., Oh, S. Y., Lee, E. Y., MacFall, J. R., … Kim, K. W. (2014). Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance imaging. Neuroradiology, 56 (4), 265–81. doi:10.1007/s00234-014-1322-6.
    1. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging. 2001;20(1):45–57. doi: 10.1109/42.906424.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever