Clustering Nuclei Using Machine Learning Techniques

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dc.contributor.author Peng, Yu en_US
dc.contributor.author Park, Mira en_US
dc.contributor.author Xu, Min en_US
dc.contributor.author Luo, Suhuai en_US
dc.contributor.author Jin, Jesse Sheng en_US
dc.contributor.author Cui, Yue en_US
dc.contributor.author Felix, W en_US
dc.contributor.author Santos, Leonardo D en_US
dc.contributor.editor Yan Li a?? Jiajia Yang a?? Peng Wen a?? Jinglong Wu en_US
dc.date.accessioned 2012-02-02T11:10:19Z
dc.date.available 2012-02-02T11:10:19Z
dc.date.issued 2010 en_US
dc.identifier 2009008153 en_US
dc.identifier.citation Peng Yu et al. 2010, 'Clustering Nuclei Using Machine Learning Techniques', , IEEE Computer Society, Gold Coast Australia, , pp. 52-57. en_US
dc.identifier.issn 978-1-4244-6843-0 en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16545
dc.description.abstract Cervical cancer is the second most common cancer among women. Meanwhile, cervical cancer could be largely preventable and curable with regular Pap tests. Nuclei changes in the cervix could be found by this test. Accurate nuclei detection is extremely critical as it is the previous step of analysing nuclei changes and diagnosis afterwards. Recently, computer-aided nuclei segmentation has increased dramatically. Athough such algorithms could be utilised in the situation for spare nuclei since they are intuitively detected, the segmentation for the complicated nuclei clusters is still challenging task. This paper presents a new methodology for the detection of cervical nuclei clusters. We first detect all the nuclei from the cervical microscopic image by an ellipse fitting algorithm. Second, we chose some high-relevant features from all the the features we obtained in last step via F-score, which is based on to what extent one feature attributes to results. All the ellipses are then classified into single ones and cluster ones by C4.5 decision tree with selected features. We evaluated the performance of this method by the classification accuracy, sensitivity, and cluster predictive value. With the 9 selected features fromt he original 13 features, we came by the promising classification accuracy (97,8%). en_US
dc.language English en_US
dc.publisher IEEE Computer Society en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/ICCME.2010.5558874 en_US
dc.title Clustering Nuclei Using Machine Learning Techniques en_US
dc.parent 2010 IEEE/ICME International Conference on Complex Medical Engineering en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Gold Coast Australia en_US
dc.identifier.startpage 52 en_US
dc.identifier.endpage 57 en_US
dc.cauo.name FEIT.School of Computing and Communications en_US
dc.conference Verified OK en_US
dc.for 110000 en_US
dc.personcode 0000063729 en_US
dc.personcode 118435 en_US
dc.personcode 109684 en_US
dc.personcode 103657 en_US
dc.personcode 0000022523 en_US
dc.personcode 0000064249 en_US
dc.personcode 0000064247 en_US
dc.personcode 0000064250 en_US
dc.percentage 100 en_US
dc.classification.name Medical And Health Sciences en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE/ICME International Conference on Complex Medical Engineering en_US
dc.date.activity 20100713 en_US
dc.location.activity Gold Coast Australia en_US
dc.description.keywords NA en_US


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