Morphological granulometric features of nucleus in automatic bone marrow white blood cell classification.
IEEE Trans Inf Technol Biomed
; 11(3): 353-9, 2007 May.
Article
em En
| MEDLINE
| ID: mdl-17521086
ABSTRACT
The 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.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Células da Medula Óssea
/
Interpretação de Imagem Assistida por Computador
/
Núcleo Celular
/
Contagem de Leucócitos
/
Leucócitos
Tipo de estudo:
Diagnostic_studies
Limite:
Humans
Idioma:
En
Revista:
IEEE Trans Inf Technol Biomed
Ano de publicação:
2007
Tipo de documento:
Article