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dc.contributor.authorWiliem, Arnold
dc.contributor.authorSanderson, Conrad
dc.contributor.authorWong, Yongkang
dc.contributor.authorHobson, Peter
dc.contributor.authorMinchin, Rodney F
dc.contributor.authorLovell, Brian C
dc.date.accessioned2020-07-30T23:10:02Z
dc.date.available2020-07-30T23:10:02Z
dc.date.issued2014
dc.identifier.issn0031-3203
dc.identifier.doi10.1016/j.patcog.2013.10.014
dc.identifier.urihttp://hdl.handle.net/10072/395959
dc.description.abstractThis paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (e.g., speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is composed of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom2315
dc.relation.ispartofpageto2324
dc.relation.ispartofissue7
dc.relation.ispartofjournalPattern Recognition
dc.relation.ispartofvolume47
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchcode4609
dc.titleAutomatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationWiliem, A; Sanderson, C; Wong, Y; Hobson, P; Minchin, RF; Lovell, BC, Automatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching, Pattern Recognition, 2014, 47 (7), pp. 2315-2324
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2020-07-29T06:57:05Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2014 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorSanderson, Conrad


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