A new approach for fully automated segmentation of peripheral blood smears

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dc.authoridElen, Abdullah/0000-0003-1644-0476
dc.contributor.authorElen, Abdullah
dc.contributor.authorTuran, Muhammed Kamil
dc.date.accessioned2024-09-29T16:06:42Z
dc.date.available2024-09-29T16:06:42Z
dc.date.issued2018
dc.departmentKarabük Üniversitesien_US
dc.description.abstractPeripheral blood smear is microscopically examining technique for blood samples from patients by painting special dyes in clinic laboratories. Blood diseases can be diagnosed by examining morphology, numbers and percentages of leukocyte, erythrocyte and thrombocyte cells in blood samples. However, this method is a considerably time-consuming process and requires an evaluation performed by a hematology specialist. It is not often provided a definitive assessment due to the expert's clinical experience and judgment during review. Although there are considerable studies about the segmentation of blood smear images in the literature, there is no method to segment all blood cells. In this study, a new segmentation algorithm is proposed, which automatically extracts leukocyte, erythrocyte and thrombocyte cells from peripheral blood smear images. Purpose of this study here is to make highly accurate and complete blood count. The algorithm treats each image as a universal set and represents each object in the image as a subset as a result of the applied operations. In the developed method, leukocytes and thrombocytes achieve better success than other studies. However, it has been observed that the average success rate of stacked erythrocytes decreases. Statistical tests of the developed method were performed using 200 blood smear images in experimental studies. According to the obtained results, it is seen that high accuracy (leukocyte 99.86%, thrombocyte 98.4%, erythrocyte 93.4%) and precision (leukocyte 94.77%, thrombocyte 90.14%, erythrocyte 95.88%) were achieved in all three blood cells. (C) 2017 The Authors. Published by IASE.en_US
dc.description.sponsorshipKarabuk University [KBU-BAP-15/2-DR-003]en_US
dc.description.sponsorshipThis work was supported by Research Fund of the Karabuk University, Project Number: KBU-BAP-15/2-DR-003.en_US
dc.identifier.doi10.21833/ijaas.2018.01.011
dc.identifier.endpage93en_US
dc.identifier.issn2313-626X
dc.identifier.issn2313-3724
dc.identifier.issue1en_US
dc.identifier.startpage81en_US
dc.identifier.urihttps://doi.org/10.21833/ijaas.2018.01.011
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6997
dc.identifier.volume5en_US
dc.identifier.wosWOS:000428119000011en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherInst Advanced Science Extensionen_US
dc.relation.ispartofInternational Journal of Advanced and Applied Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBlood cell segmentationen_US
dc.subjectAutomatic blood analysesen_US
dc.subjectPeripheral blood smearen_US
dc.subjectGraham scanen_US
dc.subjectMedical image processingen_US
dc.titleA new approach for fully automated segmentation of peripheral blood smearsen_US
dc.typeArticleen_US

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