A Novel Probabilistic Nuclei Segmentation Algorithm for H&E Stained Histopathological Tissue Images

dc.authoridSerin, Faruk/0000-0002-1458-4508
dc.authoridGül, Mehmet/0000-0002-1374-0783
dc.authorwosidSerin, Faruk/AAZ-2560-2020
dc.authorwosidGül, Mehmet/ABI-6336-2020
dc.contributor.authorSerin, Faruk
dc.contributor.authorErturkler, Metin
dc.contributor.authorGul, Mehmet
dc.date.accessioned2024-08-04T20:10:32Z
dc.date.available2024-08-04T20:10:32Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, we propose a novel, fast and accurate segmentation algorithm to segment nuclei in H&E stained histopathological tissue images. The proposed algorithm does not require pre-processing, post-processing, and any manual parameter or threshold. The algorithm utilizes probabilistic and statistical properties of the pixels' color value in the images with RGB color space, and determines whether pixels are a part of any nuclei or not by using an automatically calculated threshold value. The algorithm provides time efficiency and reduced overall cost in the segmentation. Two more algorithms are also proposed to distinguish nuclei cluster from the other clusters obtained by K-means, and eliminate false positives in nuclei cluster, which are not nuclei. In order to compare and evaluate the performance of the proposed segmentation algorithm in terms of time and cost efficiency, K -Means is preferred because of its common usage. Expert evaluation is declared as ground truth for determining the accuracy of the results. The experiments are performed on 60 healthy and 60 damaged kidney, and 60 healthy and 60 damaged liver tissue images. The evaluations show that the proposed algorithm can effectively segment nuclei. The comparison results also demonstrate that the deviation between proposed algorithm and the expert is 2%, while the deviation between K -Means and expert is 5%.en_US
dc.identifier.doi10.2339/politeknik.464541
dc.identifier.endpage17en_US
dc.identifier.issn1302-0900
dc.identifier.issn2147-9429
dc.identifier.issue1en_US
dc.identifier.startpage7en_US
dc.identifier.trdizinid419344en_US
dc.identifier.urihttps://doi.org/10.2339/politeknik.464541
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/419344
dc.identifier.urihttps://hdl.handle.net/11616/92846
dc.identifier.volume23en_US
dc.identifier.wosWOS:000506639500002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherGazi Univen_US
dc.relation.ispartofJournal of Polytechnic-Politeknik Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectImage segmentationen_US
dc.subjectmedical image processingen_US
dc.subjectclustering methodsen_US
dc.subjectpattern recognitionen_US
dc.titleA Novel Probabilistic Nuclei Segmentation Algorithm for H&E Stained Histopathological Tissue Imagesen_US
dc.typeArticleen_US

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