Computer Aided Abnormality Detection for Microscopy Images of Cervical Tissue

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dc.contributor.author Cui, Y
dc.contributor.author Jin, J
dc.contributor.author Park, M
dc.contributor.author Luo, S
dc.contributor.author Xu, M
dc.contributor.author Peng, Y
dc.contributor.author Felix, WS
dc.contributor.author Santos, L
dc.contributor.editor Wu, YLÂJYÂPWÂJ
dc.date.accessioned 2012-02-02T11:10:18Z
dc.date.issued 2010-01
dc.identifier.citation 2010 IEEE/ICME International Conference on Complex Medical Engineering, 2010, pp. 63 - 68
dc.identifier.isbn 978-1-4244-6843-0
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/16544
dc.description.abstract Cervical cancer is the second most common malignancy among woman worldwide, if it is detected in early stage, cure rate is relatively high. Computer aided abnormality detection for cervical smear is developed to assist medical experts to handle microscopy images, examine cell abnormalities and diagnose dyskaryosis. The microscopy images of cells in cervix uteri are stained by tumor marker Ki-67, so that the abnormal nuclei present brown while normal ones are bluish. Segmentation is the most important and difficult task to calculate the ratio of abnormal nuclei to all nuclei. In order to achieve accurate segmentation of nuclei, we propose a multi-level segmentation approach for abnormality identification in microscopy images. First level segmentation aims to partition abnormal (stained) nuclei regions and all nuclei regions. Because of under-segmentation after first level segmentation, second level segmentation is applied to further partition the clustered nuclei. In order to classify touching regions of clustered nuclei and separate regions of single nucleus, relevant meaningful features are extracted from regions of interest. Consequently all the nuclei regions are separated and in conjunction with the abnormal nuclei regions in the first level segmentation, the abnormality i.e. ration of abnormal nuclei to all nuclei is obtained. Experimental results indicate that our method achieved an accuracy of 93.55% and 95.8% in term of abnormal nuclei and all nuclei respectively for identification of abnormalities. Our proposed method produces a satisfactory segmentation.
dc.publisher IEEE Computer Society
dc.relation.isbasedon 10.1109/ICCME.2010.5558872
dc.title Computer Aided Abnormality Detection for Microscopy Images of Cervical Tissue
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 63 en_US
dc.identifier.endpage 68 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
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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