A novel overlapped nuclei splitting algorithm for histopathological images

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dc.authoridGül, Mehmet/0000-0002-1374-0783
dc.authoridSerin, Faruk/0000-0002-1458-4508;
dc.authorwosidGül, Mehmet/ABI-6336-2020
dc.authorwosidSerin, Faruk/AAZ-2560-2020
dc.authorwosidErturkler, Metin/Y-1230-2019
dc.contributor.authorSerin, Faruk
dc.contributor.authorErturkler, Metin
dc.contributor.authorGul, Mehmet
dc.date.accessioned2024-08-04T20:43:58Z
dc.date.available2024-08-04T20:43:58Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description.abstractBackground and objective: Nuclei segmentation is a common process for quantitative analysis of histopathological images. However, this process generally results in overlapping of nuclei due to the nature of images, the sample preparation and staining, and image acquisition processes as well as insufficiency of 2D histopathological images to represent 3D characteristics of tissues. We present a novel algorithm to split overlapped nuclei. Methods: The histopathological images are initially segmented by K-Means segmentation algorithm. Then, nuclei cluster are converted to binary image. The overlapping is detected by applying threshold area value to nuclei in the binary image. The splitting algorithm is applied to the overlapped nuclei. In first stage of splitting, circles are drawn on overlapped nuclei. The radius of the circles is calculated by using circle area formula, and each pixel's coordinates of overlapped nuclei are selected as center coordinates for each circle. The pixels in the circle that contains maximum number of intersected pixels in both the circle and the overlapped nuclei are removed from the overlapped nuclei, and the filled circle labeled as a nucleus. Results: The algorithm has been tested on histopathological images of healthy and damaged kidney tissues and compared with the results provided by an expert and three related studies. The results demonstrated that the proposed splitting algorithm can segment the overlapping nuclei with accuracy of 84%. Conclusions: The study presents a novel algorithm splitting the overlapped nuclei in histopathological images and provides more accurate cell counting in histopathological analysis. Furthermore, the proposed splitting algorithm has the potential to be used in different fields to split any overlapped circular patterns. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.cmpb.2017.08.010
dc.identifier.endpage70en_US
dc.identifier.issn0169-2607
dc.identifier.issn1872-7565
dc.identifier.pmid28947006en_US
dc.identifier.scopus2-s2.0-85028360599en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage57en_US
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2017.08.010
dc.identifier.urihttps://hdl.handle.net/11616/97949
dc.identifier.volume151en_US
dc.identifier.wosWOS:000411457800007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevier Ireland Ltden_US
dc.relation.ispartofComputer Methods and Programs in Biomedicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCADen_US
dc.subjectOverlapped nucleien_US
dc.subjectNuclei splittingen_US
dc.subjectHistopathological analysisen_US
dc.subjectNuclei detectionen_US
dc.subjectCell nuclei countingen_US
dc.titleA novel overlapped nuclei splitting algorithm for histopathological imagesen_US
dc.typeArticleen_US

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