Determining cellularity status of tumors based on histopathology using hybrid image segmentation

DSpace/Manakin Repository

Search OPUS


Advanced Search

Browse

My Account

Show simple item record

dc.contributor.author Tafavogh, S
dc.contributor.author Kennedy, PJ
dc.contributor.author Catchpoole, DR
dc.date.accessioned 2014-04-03T01:05:23Z
dc.date.issued 2012
dc.identifier.citation Proceedings of the International Joint Conference on Neural Networks, 2012
dc.identifier.isbn 9781467314909
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/22186
dc.description.abstract A Computer Aided Diagnosis (CAD) system is developed to determine cellularity status of a tumor. The system helps pathologists to distinguish a tumor with cell proliferation from normal tumors. The developed CAD system implements a hybrid segmentation method to identify and extract the morphological features that are used by pathologists for determining cellularity status of tumor. Adaptive Mean Shift (AMS) clustering as a non-parametric technique is integrated with Color Template Matching (CTM) to construct segmentation approach. We used Expectation Maximization (EM) clustering as a parametric technique for the sake of comparison with our proposed approach. The output of our proposed system and EM are validated by two pathologists as ground truth. The result of our developed system is quite close to the decision of pathologists, and it significantly outperforms EM in terms of accuracy. © 2012 IEEE.
dc.relation.isbasedon 10.1109/IJCNN.2012.6252768
dc.title Determining cellularity status of tumors based on histopathology using hybrid image segmentation
dc.type Conference Proceeding
dc.description.version Published
dc.parent Proceedings of the International Joint Conference on Neural Networks
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 8 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 080106 Image Processing
dc.for 060102 Bioinformatics
dc.personcode 990679
dc.personcode 996701
dc.personcode 112055
dc.percentage 50 en_US
dc.classification.name Bioinformatics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Joint Conference on Neural Networks en_US
dc.date.activity 20120610 en_US
dc.location.activity Brisbane, Australia 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 Software
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true


Files in this item

This item appears in the following Collection(s)

Show simple item record