IMAGE ANALYSIS

Counting white blood cells using morphological granulometries

[+] Author Affiliations
Nipon Theera-Umpon

Department of Electrical Engineering, University of Missouri–Columbia, Columbia, Missouri?65211

Paul D. Gader

Department of Computer Engineering and Computer Science, University of Missouri–Columbia, Columbia, Missouri?65211

J. Electron. Imaging. 9(2), 170-177 (Apr 01, 2000). doi:10.1117/1.482737
History: Received Mar. 23, 1999; Revised Dec. 8, 1999; Accepted Feb. 23, 2000
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Abstract

We describe a modification of the mixture proportion estimation algorithm based on the granulometric mixing theorem. The modified algorithm is applied to the problem of counting different types of white blood cells in bone marrow images. In principle, the algorithm can be used to count the proportion of cells in each class without explicitly segmenting and classifying them. The direct application of the original algorithm does not converge well for more than two classes. The modified algorithm uses prior statistics to initially segment the mixed pattern spectrum and then applies the one-primitive estimation algorithm to each initial component. Applying the algorithm to one class at a time results in better convergence. The counts produced by the modified algorithm on six classes of cells—myeloblast, promyelocyte, myelocyte, metamyelocyte, band, and PolyMorphoNuclear (PMN)—are very close to the human expert’s numbers; the deviation of the algorithm counts is similar to the deviation of counts produced by human experts. The important technical contributions are that the modified algorithm uses prior statistics for each shape class in place of prior knowledge of the total number of objects in an image, and it allows for more than one primitive from each class. © 2000 SPIE and IS&T.

© 2000 SPIE and IS&T

Topics

Blood

Citation

Nipon Theera-Umpon and Paul D. Gader
"Counting white blood cells using morphological granulometries", J. Electron. Imaging. 9(2), 170-177 (Apr 01, 2000). ; http://dx.doi.org/10.1117/1.482737


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