A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

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dc.citation.epage490en_US
dc.citation.issueNumber6en_US
dc.citation.spage480en_US
dc.citation.volumeNumber85en_US
dc.contributor.authorArslan, S.en_US
dc.contributor.authorOzyurek, E.en_US
dc.contributor.authorGunduz Demir, C.en_US
dc.date.accessioned2016-02-08T11:03:17Z
dc.date.available2016-02-08T11:03:17Z
dc.date.issued2014en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractComputer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. © 2014 International Society for Advancement of Cytometry.en_US
dc.identifier.doi10.1002/cyto.a.22457en_US
dc.identifier.issn1552-4922
dc.identifier.urihttp://hdl.handle.net/11693/26678
dc.language.isoEnglishen_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/10.1002/cyto.a.22457en_US
dc.source.titleCytometry. Part Aen_US
dc.subjectBlastsen_US
dc.subjectBone marrow imagesen_US
dc.subjectCell segmentationen_US
dc.subjectLeukemiaen_US
dc.subjectMarker-controlled watershedsen_US
dc.subjectMicroscopyen_US
dc.subjectPeripheral blood imagesen_US
dc.subjectWhite blood cellsen_US
dc.subjectAlgorithmen_US
dc.subjectAutomated pattern recognitionen_US
dc.subjectBone marrow cellen_US
dc.subjectHumanen_US
dc.subjectImage enhancementen_US
dc.subjectImage processingen_US
dc.subjectLeukocyteen_US
dc.titleA color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow imagesen_US
dc.typeArticleen_US

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