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Computer-Assisted Automatic Segmentation/Detection of Multiple Sclerosis Lesions in Brain MR Images: A Review

Rupali S. Kamathe, Kalyani Joshi

Abstract


In humans, central nervous system (CNS) may suffer with demyelinating disease, named multiple sclerosis (MS), characterized by a wide scope of neurological deficits. Magnetic resonance imaging (MRI) is an important tool for MS diagnosis which enables the detection of location and size of the affected tissue. Research on this disease is has grabbed wide attention so as to improve the treatment plans by better understanding of causes and the disease progression. The diagnostic accuracy can be improved with automatic segmentation of MS lesions. It offers a promising alternative to manual detection which is affected due to expert to expert variation, the experience of individual and is definitely a time-consuming task. The objective of this paper is to review the image processing based approaches to automate MS lesion detection in human brain MRI. This paper covers exhaustive review of different image processing algorithms or techniques used; and provide a detailed comparison of results which will enable researchers in the field, to understand the scope and challenges involved towards automatic detection and classification of MS lesions from MRI scans of brain.

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References


X. T-Fernandez, S.K. Warfield. “A model of population and subject (MOPS) intensities with application to multiple sclerosis lesion segmentation, IEEE Trans Med Imaging. 20155; 34(6): 1349–61p.

D. Biediger, C. Collet, J.-P. Amspach. Multiple sclerosis lesion detection with local multimodal Markovian analysis and cellular automata ‘GrowCut’, J Comput Surg. 2014; 1: 3p.

C. Elliott, D.L. Arnold, D.L. Collins, T. Arbel. Temporally consistent probabilistic detection of new multiple sclerosis lesions in brain MRI, IEEE Trans Med Imaging. 2013; 32(8): 1490–1503p.

M. Cabezas, A. Oliver, J. Freixenet, X. Llado. A Supervised Approach for Multiple Sclerosis Lesion Segmentation Using Context Features and an Outlier Map. Springer Berlin Heidelberg; 2013, 782–9p.

M.D. Steenwijk, P.J.W. Pouwels, M. Daams, J.W. Dalen, M.W.A. Caan, E. Richard, F. Barkhof, H. Vrenken. Accurate white matter lesion segmentation by K-nearest neighbor classification with tissue type priors (kNN-TTPs), NeuroImage: Clin. 2013; 3: 462–9p.

S. Datta, P.A. Narayana. A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis, NeuroImage: Clin. 2013; 2: 184–96p.

C.P. Loizou, E.C. Kyriacou, I. Seemenis, M. Pantziaris, S. Petroudi, M. Karaolis, C. Pattichis. Brain white matter lesion classification in multiple sclerosis subjects for the prognosis of future disability, Intell Decision Technol. 2013; 7: 3–10p. DOI 10.3233/IDT-120147 IOS Press.

Z. Karimaghaloo, H. Rivaz, D.L. Arnold, D.L. Collins, T. Arbel. Temporal hierarchical adaptive texture CRF for automatic detection of gadolinium – enhancing multiple sclerosis lesions in brain MRI, IEEE Trans Med Imaging. DOI10.1109/TMI.2014.2382561.

Atlas of MS 2013: Mapping Multiple Sclerosis around the World. Multiple Sclerosis International Federation London. Available at: http://www.msif.org/about-ms/publications-and-resources/.

Z. Karimaghaloo, M. Shah, S.J. Francis, D.L. Arnold, D.L. Collins, T. Arbel. Automatic detection of gadolinium-enhancing multiple sclerosis lesions in brain MRI using conditional random fields, IEEE Trans Med Imaging. 2012; 31(6): 1181–94p.

Multiple Sclerosis Society of Canada, Multiple Sclerosis: Its effects on you and those you love, 2012.

D. G-Lorenzo, S. Prima, D.L. Arnold, D.L. Collins, C. Barillot. Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis, IEEE Trans Med Imaging. 2011; 30(8): 1455–67p.

C.P. Loizou, V. Murray, M.S. Pattichis, I. Seimenis, M. Pantziaris, C.S. Pattichis. multiscale amplitude-modulation frequency-modulation (AM–FM) texture analysis of multiple sclerosis in brain MRI images, IEEE Trans Inform Technol Biomedi. 2011; 15(1): 119–29p.

Z. Zeng, R. Zwiggelaar. Joint Histogram Modelling for Segmentation Multiple Sclerosis Lesions. Berlin Heidelberg: Springer; 2011, 133–44p.

H. Khotanlou, M. Afrasiabi. Segmentation of multiple sclerosis lesions in brain MR images using spatially constained possibilistic fuzzy C-means classification, J Med Signals Sens. 2011; I(3): 149–55p.

A. A-Ballin, M. Galun, J.M. Gomori, M. Filippi, P. Valsasina, R. Basri, A. Brandt. Automatic segmentation and classification of multiple sclerosis in multichannel MRI, IEEE Trans Biomed Eng. 2009; 56(10): 2461–9p.

R. de Boer, H.A. Vrooman, F. Lijn, M.W. Vernooij, M.A. Ikram, A. Lugt, M.M.B. Breteler, W.J. Niessen. White matter lesion extension to automatic brain tissue segmentation on MRI, NeuroImage. 2009; 45: 1151–61p.

J. Lecoeur, J.C. Ferre, C. Barillot. Optimized supervised segmentation of MS lesions from multispectral MRIs, In: MICCAI Workshop on Medical Image Analysis on Multiple Sclerosis (Validation and Methodological Issues). UK; 2009.

O. Freifeld, H. Greenspan, J. Goldberger. Multiple sclerosis lesion detection using constrained GMM and curve evolution, Int J Biomed Imaging. 2009. DOi:10.1155/2009/715124.

P. Anbeek, K.L. Vincken, M.A. Viergever. Automated MS-lesion segmentation by KNearest neighbor classification, Med Image Comput Comput–Assisted Interv. 2008.

R. Khayati, M. Vafadust, F. Towhidkhah, S.M. Nabavi. 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: 379–90p.

Z. Lao, D. Shen, D. Liu, A.F. Jawad, E.R. Melhem, L.J. launer, R.N. Bryan, C. Davatzikos. Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine, Acad Radiol. 2008; 15(3). Doi:10.1016/j.acra.2007.10.012.

N. Shiee, P.-L. Bazin, D.L. Pham. Multiple sclerosis lesion segmentation using statistical and topological atlases, 2008,” Insight J. [http://hdl.handle.net/1926/1442].

D. G-lorenzo, S. Prima, S.P. Morrissey, C. barillot. A robust expectation-maximization algorithm for multiple sclerosis lesion segmentation, 2008.

M.A. Horsfield, R. Bakshi, M. Rovaris, M.A. Rocca, V.S.R. Dandamudi, P. Valsasina, E. Judica, F. Lucchini, R.G. Guttmann, M.P. Sormani, M. Filippi. Incorporating domain knowledge into the fuzzy connectedness framework: application to brain lesion volume estimation in multiple sclerosis, IEEE Trans Med Imaging. 2007; 26(12): 1670–80p.

J.J. Corso, A. Yuille, N.L. Sicotte, A. Toga. Detection and Segmentation of Pathological Structures by the Extended Graph-Shifts Algorithm. MICCAI, Berlin Heidelberg: Springer; 2007, 985–93p.

Y. Wu, S.K. Warfield, I.L. Tan, W.M. Wells, III, D.S. Meier, R.A. Schijndel, F. Barkhof, C.R.G. Guttmann. Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI, NeuroImage. 2006; 32: 1205–15p.

L. Li, X. Li, H. Lu, W. Huang, C. Christodoulou, A. Tudorica, L.B. Krupp, Z. Liang. MRI volumetric analysis of multiple sclerosis: methodology and validation, IEEE Trans Nucl Sci. 2003; 50(5): 1686–91p.

A.P. Zijdenbos, R. Forghani, A.C. Evans. Automatic pipeline analysis of 3-D MRI data for clinical trials: application to multiple sclerosis, IEEE Trans Med Imaging. 2002; 21(10): 1280–91p.

K.V. Leemput, F. Maes, D. Vandermeulen, A. Colchester, P. Suetens. Automated segmentation of multiple sclerosis lesions by model outlier detection, IEEE Trans Med Imaging. 2001; 20(8): 677–88p.

B. Johnston, M.S. Atkins, B. Mackiewich, M. Anderson. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI, IEEE Trans Med Imaging. 1996; 15(2): 154–69p.

Multiple sclerosis information for health and social care professionals. Fourth edition Multiple Sclerosis Trust 2011, www.mstrust.org.uk.

The McConnell Brain Imaging Center: http://www.bic.mni.mcgill.ca/brainweb/.

The MICCAI (Medical Image Computing and Computer Assisted Intervention) Grand Challenge 2008 on MS Lesions Segmentations: www.nitrc.org/projects/msseg.

Multiple sclerosis information for health and social care professionals. Fourth edition Multiple Sclerosis Trust 2011.




DOI: https://doi.org/10.37628/ijoippr.v3i1.214

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