Publication:
Comparative Analysis on Blood Cell Image Segmentation

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dc.ConferencedateDEC 01-02, 2013
dc.ConferencelocationSingapore, SINGAPORE
dc.Conferencename2nd International Symposium on Computer, Communication, Control and Automation (3CA)
dc.contributor.authorMuda, TZTen_US
dc.contributor.authorSalam, RAen_US
dc.date.accessioned2024-05-29T02:53:23Z
dc.date.available2024-05-29T02:53:23Z
dc.date.issued2013
dc.description.abstractImage segmentation is an important phase in image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present a comparative analysis on several segmentation algorithms. Three selected common approaches, that are Fuzzy c-means, K-means and Mean-shift were presented. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method that is K-means was selected. K-means has been enhanced by integrating Median-cut algorithm to further improve the segmentation process. The proposed integrated method has shown a significant improvement in the number of selected regions.
dc.identifier.epage477
dc.identifier.issn1951-6851
dc.identifier.scopusWOS:000335510100115
dc.identifier.spage474
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/11387
dc.identifier.volume68
dc.languageEnglish
dc.language.isoen_US
dc.publisherAtlantis Pressen_US
dc.relation.ispartofProceedings Of The 2nd International Symposium On Computer, Communication, Control And Automation
dc.sourceWeb Of Science (ISI)
dc.subjectSegmentationen_US
dc.subjectBlood Cell Imagesen_US
dc.subjectMeans-shiften_US
dc.subjectFuzzy c-meansen_US
dc.subjectK-meansen_US
dc.subjectMedian-cuten_US
dc.titleComparative Analysis on Blood Cell Image Segmentation
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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