Publication:
Adaptive Hybrid Blood Cell Image Segmentation

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dc.ConferencecodeUTM, Inst Noise & Vibrat
dc.ConferencedateDEC 03-05, 2018
dc.ConferencelocationMALAYSIA
dc.ConferencenameEngineering Application of Artificial Intelligence Conference (EAAIC)
dc.contributor.authorMuda, TZTen_US
dc.contributor.authorSalam, RAen_US
dc.contributor.authorIsmail, Sen_US
dc.date.accessioned2024-05-29T02:53:18Z
dc.date.available2024-05-29T02:53:18Z
dc.date.issued2019
dc.description.abstractImage segmentation is an important phase in the 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 an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of regions.
dc.identifier.doi10.1051/matecconf/201925501001
dc.identifier.issn2261-236X
dc.identifier.scopusWOS:000468561800001
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/11382
dc.identifier.volume255
dc.languageEnglish
dc.language.isoen_US
dc.publisherE D P Sciencesen_US
dc.relation.ispartofEngineering Application Of Artificial Intelligence Conference 2018 (Eaaic 2018)
dc.sourceWeb Of Science (ISI)
dc.titleAdaptive Hybrid Blood Cell Image Segmentation
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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