A new approach for unsupervised segmentation
Applied Soft Computing
Methods toward in vivo measurement of zebrafish epithelial and deep cell proliferation
Computer Methods and Programs in Biomedicine
Automatic classification of lymphoma images with transform-based global features
IEEE Transactions on Information Technology in Biomedicine
Automatic recognition of five types of white blood cells in peripheral blood
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part II
A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images
Journal of Systems and Software
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Automatic cervical cell segmentation and classification in Pap smears
Computer Methods and Programs in Biomedicine
Machine Vision and Applications
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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