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dc.contributor.authorFiot, Jean-Baptiste
dc.contributor.authorCohen, Laurent D.
dc.contributor.authorRaniga, Parnesh
dc.contributor.authorFripp, Jurgen
dc.date.accessioned2012-02-03T15:00:28Z
dc.date.available2012-02-03T15:00:28Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/8026
dc.language.isoenen
dc.subjectBrain Imagingen
dc.subjectSupport Vector Machinesen
dc.subjectClassificationen
dc.subjectSegmentationen
dc.subjectLesionen
dc.subject.ddc621.3en
dc.titleEfficient Lesion Segmentation using Support Vector Machinesen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherCSIRO Information and Commuciation Technologies (CSIRO ICT Centre) http://www3.ict.csiro.au/ CSIRO;Australie
dc.description.abstractenSupport Vector Machines (SVM) are a machine learning technique that has been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities (WMH). Current approaches using SVM for WMH segmentation extract features from the brain and classify these followed by complex post-processing steps to remove false positives. The method presented in this paper combines the use of domain knowledge, advanced pre-processing (based on tissue segmentation and atlas propagation) and SVM classification to obtain efficient and accurate WMH segmentation. Features generated from up to four MR modalities (T1-w, T2-w, PD and FLAIR), differing neighbourhood sizes and the use of multi-scale features were compared. We found that although using all 4 modalities gave the best overall classification (average Dice scores of 0.54 ± 0.12, 0.72 ± 0.06 and 0.82 ± 0.06 respectively for small, moderate and severe lesion loads, using 3x3x3 neighbourhood intensity features); this was not significantly different (p = 0.50) from using just T1-w and FLAIR sequences (Dice scores of 0.52 ± 0.13, 0.71 ± 0.08 and 0.81 ± 0.07 for the same lesion loads and feature type). Furthermore, there was a negligible difference between using 5x5x5 and 3x3x3 features (p = 0.93). Finally, we show that careful consideration of features and preprocessing techniques leads to more efficient classification which outperforms the one based on all features with post-processing, and also saves storage space and computation time.en
dc.relation.ispartoftitleComputational Vision and Medical Image Processing: VipIMAGE 2011en
dc.relation.ispartofeditorJorge, R.M. Natal
dc.relation.ispartofeditorTavares, João Manuel R.S.
dc.relation.ispartofpublnameCRC Pressen
dc.relation.ispartofpublcityLeidenen
dc.relation.ispartofdate2012
dc.relation.ispartofpages440en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00662344en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelTraitement du signalen
dc.relation.ispartofisbn978-0-415-68395-1en
dc.relation.conftitleVIPIMAGE 2011, Third ECCOMAS Thematic Conference on Computational Vision and Medical Image Processingen
dc.relation.confdate2011-09
dc.relation.confcityOlhãoen
dc.relation.confcountryPortugalen


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