Determining Cellularity Status of Tumors based on Histopathology using Hybrid Image Segmentation

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dc.contributor.author Tafavogh, Siamak en_US
dc.contributor.author Kennedy, Paul en_US
dc.contributor.author Catchpoole, Daniel en_US
dc.contributor.editor N/A en_US
dc.date.accessioned 2014-04-03T01:05:23Z
dc.date.available 2014-04-03T01:05:23Z
dc.date.issued 2012 en_US
dc.identifier 2011007488 en_US
dc.identifier.citation Tafavogh, Siamak, Kennedy, Paul, and Catchpoole, Daniel 2012, 'Determining Cellularity Status of Tumors based on Histopathology using Hybrid Image Segmentation', IEEE, USA, pp. 1-8. en_US
dc.identifier.issn 2161-4393 en_US
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 en_US
dc.language English en_US
dc.publisher IEEE en_US
dc.relation.isbasedon http://dx.doi.org/10.1109/IJCNN.2012.6252768 en_US
dc.title Determining Cellularity Status of Tumors based on Histopathology using Hybrid Image Segmentation en_US
dc.parent International Joint Conference on Neural Networks en_US
dc.journal.volume en_US
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 060102 en_US
dc.personcode 11092754 en_US
dc.personcode 990679 en_US
dc.personcode 996701 en_US
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
dc.description.keywords en_US
dc.staffid 996701 en_US


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