Clustering Nuclei Using Machine Learning Techniques

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dc.contributor.author Peng, Y
dc.contributor.author Park, M
dc.contributor.author Xu, M
dc.contributor.author Luo, S
dc.contributor.author Jin, J
dc.contributor.author Cui, Y
dc.contributor.author Felix, WS
dc.contributor.author Santos, L
dc.contributor.editor Wu, YLÂJYÂPWÂJ
dc.date.accessioned 2012-02-02T11:10:19Z
dc.date.issued 2010-01
dc.identifier.citation 2010 IEEE/ICME International Conference on Complex Medical Engineering, 2010, pp. 52 - 57
dc.identifier.isbn 978-1-4244-6843-0
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%).
dc.publisher IEEE Computer Society
dc.relation.isbasedon 10.1109/ICCME.2010.5558874
dc.title Clustering Nuclei Using Machine Learning Techniques
dc.type Conference Proceeding
dc.parent 2010 IEEE/ICME International Conference on Complex Medical Engineering
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.conference IEEE/ICME International Conference on Complex Medical Engineering
dc.for 0903 Biomedical Engineering
dc.personcode 103657
dc.personcode 109684
dc.personcode 118435
dc.percentage 100 en_US
dc.classification.name Biomedical Engineering 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.date.activity 2010-07-13
dc.location.activity Gold Coast Australia en_US
dc.description.keywords NA en_US
dc.description.keywords sex, domestic worker, China, cultural politics
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computing and Communications
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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