Show simple item record

dc.contributor.authorSchaefer, Gerald
dc.contributor.authorDoshi, Niraj P.
dc.contributor.authorZhu, Shao Ying
dc.contributor.authorHu, Qinghua
dc.date.accessioned2016-11-21T11:54:44Z
dc.date.available2016-11-21T11:54:44Z
dc.date.issued2014-08
dc.identifier.citationSchaefer, G. et al (2014) 'Analysis of HEp-2 images using MD-LBP and MAD-bagging', Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Chicago: USA, 26-30 August.en
dc.identifier.isbn9781424479290
dc.identifier.doi10.1109/EMBC.2014.6944562
dc.identifier.urihttp://hdl.handle.net/10545/620920
dc.description.abstractIndirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/6944562/en
dc.relation.urlhttp://embc.embs.org/2014/en
dc.subjectHistogramsen
dc.subjectCellular biophysicsen
dc.titleAnalysis of HEp-2 images using MD-LBP and MAD-baggingen
dc.typeMeetings and Proceedingsen
dc.contributor.departmentLoughborough Universityen
dc.contributor.departmentUniversity of Derbyen
dc.identifier.journalProceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)en
html.description.abstractIndirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.


This item appears in the following Collection(s)

Show simple item record