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dc.contributor.authorBeheshti, Maedeh
dc.contributor.authorAshapure, Akash
dc.contributor.authorRahnemoonfar, Maryam
dc.contributor.authorFaichney, Jolon
dc.date.accessioned2019-05-29T12:43:30Z
dc.date.available2019-05-29T12:43:30Z
dc.date.issued2018
dc.identifier.issn1064-1246
dc.identifier.doi10.3233/JIFS-17466
dc.identifier.urihttp://hdl.handle.net/10072/375084
dc.description.abstractAccurate segmentation of fluorescence images has become increasingly important for recognizing cell nucleus that have the phenotype of interest in biomedical applications. In this study an ensemble based method is proposed for the segmentation of cell cancer microscopy images. The ensemble is constructed and compared using Bayes graph-cut algorithm, binary graph-cut algorithm, spatial fuzzy C-means, and fuzzy level set algorithm, which were chosen for their accuracy and efficiency in the segmentation area. We investigate the performance of each method separately and finally compare the results with the ensemble method. Experiments are conducted over two datasets with different cell types. At 95% confidence level, the ensemble based method represents the best among all the implemented algorithms. Also ensemble method depicts better results in comparison with other state-of-the-art segmentation methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIOS Press
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom2563
dc.relation.ispartofpageto2578
dc.relation.ispartofissue4
dc.relation.ispartofjournalJournal of Intelligent and Fuzzy Systems
dc.relation.ispartofvolume34
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode1702
dc.titleFluorescence microscopy image segmentation based on graph and fuzzy methods: A comparison with ensemble method
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorFaichney, Jolon B.
gro.griffith.authorBeheshti, Maedeh


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