Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/60907
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dc.contributor.authorNipon Theera-Umponen_US
dc.contributor.authorSompong Dhompongsaen_US
dc.date.accessioned2018-09-10T04:01:08Z-
dc.date.available2018-09-10T04:01:08Z-
dc.date.issued2007-05-01en_US
dc.identifier.issn10897771en_US
dc.identifier.other2-s2.0-34248575676en_US
dc.identifier.other10.1109/TITB.2007.892694en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=34248575676&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/60907-
dc.description.abstractThe proportion of counts of different types of white blood cells in the bone marrow, called differential counts, provides invaluable information to doctors for diagnosis. Due to the tedious nature of the differential white blood cell counting process, an automatic system is preferable. In this paper, we investigate whether information about the nucleus alone is adequate to classify white blood cells. This is important because segmentation of nucleus is much easier than the segmentation of the entire cell, especially in the bone marrow where the white blood cell density is very high. In the experiments, a set of manually segmented images of the nucleus are used to decouple segmentation errors. We analyze a set of white-blood-cell-nucleus-based features using mathematical morphology. Fivefold cross validation is used in the experiments in which Bayes' classifiers and artificial neural networks are applied as classifiers. The classification performances are evaluated by two evaluation measures: traditional and classwise classification rates. Furthermore, we compare our results with other classifiers and previously proposed nucleus-based features. The results show that the features using nucleus alone can be utilized to achieve a classification rate of 77% on the test sets. Moreover, the classification performance is better in the classwise sense when the a priori information is suppressed in both the classifiers. © 2007 IEEE.en_US
dc.subjectBiochemistry, Genetics and Molecular Biologyen_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.titleMorphological granulometric features of nucleus in automatic bone marrow white blood cell classificationen_US
dc.typeJournalen_US
article.title.sourcetitleIEEE Transactions on Information Technology in Biomedicineen_US
article.volume11en_US
article.stream.affiliationsChiang Mai Universityen_US
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