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dc.contributor.authorBeheshti, Maedehen_US
dc.contributor.authorFaichney, Jolonen_US
dc.contributor.authorGharipour, Aminen_US
dc.description.abstractThe accurate segmentation of biomedical images has become increasingly important for recognizing cells that have the phenotype of interest in biomedical applications. In order to improve the conventional deterministic segmentation models, this paper proposes a novel graph-cut cell image segmentation algorithm based on Bayes theorem. There are two segmentation phases in this method. The first phase is an interactive process to specify a preliminary set of regional pixels and the background based on the interactive graph-cut model. In the second phase, final segmentation is calculated based on the idea of Bayes theorem, combining prior information with data. Our idea can be considered an integration of graph-cut methods and Bayes theorem for cell image segmentation. Experimental results show that the proposed model performs better in comparison with several existing methods.en_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofconferencenameDICTA 2015en_US
dc.relation.ispartofconferencetitle2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA)en_US
dc.relation.ispartoflocationAdelaide, Australiaen_US
dc.subject.fieldofresearchPattern Recognition and Data Miningen_US
dc.titleBio-Cell Image Segmentation Using Bayes Graph-Cut Modelen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.facultyGriffith Sciences, School of Information and Communication Technologyen_US
gro.hasfulltextNo Full Text

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    Contains papers delivered by Griffith authors at national and international conferences.

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