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Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features

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dc.contributor.author Al Kafri A.
dc.contributor.author Sudirman S.
dc.contributor.author Hussain A.
dc.contributor.author Fergus P.
dc.contributor.author Al-Jumeily D.
dc.contributor.author Al Smadi H.
dc.contributor.author Khalaf M.
dc.contributor.author Al-Jumaily M.
dc.contributor.author Al-Rashdan W.
dc.contributor.author Bashtawi M.
dc.contributor.author Mustafina J.
dc.date.accessioned 2018-04-05T07:09:18Z
dc.date.available 2018-04-05T07:09:18Z
dc.date.issued 2017
dc.identifier.issn 0302-9743
dc.identifier.uri http://dspace.kpfu.ru/xmlui/handle/net/129649
dc.description.abstract © Springer International Publishing AG 2017. Disc herniation is a major reason for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves examining a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic labeling of lumbar disc pixels in the MRI to detect the herniation area will reduce the time to diagnose and detect the cause of LBP by the physicians. In this paper, we present a method for automatic labeling of the lumbar spine disc pixels in axial view MRI using pixels locations and gray level as features. Clinical MRIs are used for the training and testing of the method. The pixel classification accuracy and the quality of the reconstructed disc images are used as the main performance indicators for our method. Our experiments show that high level of classification accuracy of 91.1% and 98.9% can be achieved using Weighted KNN and Fine Gaussian SVM classifiers respectively.
dc.relation.ispartofseries Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.subject Disc herniation
dc.subject LBP
dc.subject Lumbar spine disc
dc.subject MRI
dc.title Lumbar spine discs labeling using axial view MRI based on the pixels coordinate and gray level features
dc.type Conference Paper
dc.relation.ispartofseries-volume 10363 LNAI
dc.collection Публикации сотрудников КФУ
dc.relation.startpage 107
dc.source.id SCOPUS03029743-2017-10363-SID85027856107


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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