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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/933322

Title
Detection of nuclei clusters from cervical cancer microscopic imagery using C4.5
Author/Creator
Peng, Yu;  Park, Mira;  Xu, Min;  Luo, Suhuai;  Jin, Jesse S.;  Cui, Yue;  Wong, W. S. Felix
Institution
The University of Newcastle. Faculty of Science & Information Technology, School of Design Communication and Information Technology
Description
Cervical cancer is the second most common cancer among women. At the same time, cervical cancer could be largely preventable and curable with regular Pap tests. This test can find nuclei changes in the cervix. Accurate nuclei detection is extremely critical as it is the previous step of analysing nuclei changes and diagnosis afterwards. In recent years, automatic nuclei segmentation has increased dramatically. Although such algorithms could be utilised in the situation for sparse 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. All the ellipses are then classified into single ones and cluster ones by C4.5 decision tree with elected features. We evaluated the performance of this method by the classification accuracy, sensitivity, and cluster predictive value. The result shown that the promising classification accuracy (97.8%) is obtained using C4.5 with 9 relative features.
Relation
2010 International Conference on Computer Engineering and Technology (ICCET 2010). Proceedings of the 2010 International Conference on Computer Engineering and Technology, Volume 7 (Chengdu, China 16-18 April, 2010) p. 593-597
Publisher Link
http://dx.doi.org/10.1109/ICCET.2010.5485792
Date
2010
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Keyword(s)
image segmentation;  cluster detection;  cervical cancer;  ellipse detection;  decision tree;  feature selection
Resource Type
conference paper
Identifier
http://hdl.handle.net/1959.13/933322
Identifier
ISBN:9781424463473
 
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Subject
"Detection of nuclei clusters from cervical cancer microscopic imagery using C4.5"
 
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