Robust recognition of white blood cell images

Author

Kovalev, Vassili A. ; Grigoriev, Andrei Y. ; Ahn, Hyo-Sok

Author_Institution

Inst. of Math., Acad. of Sci., Gomel, Byelorussia

Volume

4

fYear

1996

fDate

25-29 Aug 1996

Firstpage

371

Abstract

The objective of this work is to investigate the white blood cell (WBC) image recognition problem at all stages. A robust and effective method for automatic WBC differentiation, based on both statistical pattern recognition and neural net approaches, is presented. We demonstrate well-evaluated results ranging from image scene segmentation techniques to recognition details. Recognition accuracy on the test set of 662 images of five WBC types obtained by different imaging systems from 22 bloodstains is not less than 98%

Keywords

biological techniques; blood; cellular biophysics; feature extraction; image recognition; image segmentation; medical image processing; neural nets; object recognition; statistical analysis; automatic differentiation; bloodstains; disease diagnosis; image scene segmentation; neural net; robust recognition; statistical pattern recognition; white blood cell images; Counting circuits; Image recognition; Image segmentation; Layout; Microscopy; Optical imaging; Optical scattering; Pattern recognition; Robustness; White blood cells;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 1996., Proceedings of the 13th International Conference on

Conference_Location

Vienna

ISSN

1051-4651

Print_ISBN

0-8186-7282-X

Type

conf

DOI

10.1109/ICPR.1996.547448

Filename

547448