Automatic segmentation of cells from microscopic imagery using ellipse detection

Author

Kharma, N. ; Moghnieh, H. ; Yao, J. ; Guo, Y.P. ; Abu-Baker, A. ; Laganiere, J. ; Rouleau, G. ; Cheriet, M.

Author_Institution

Dept. of EC Eng., Concordia Univ., Montreal, Que.

Volume

1

Issue

1

fYear

2007

fDate

3/1/2007 12:00:00 AM

Firstpage

39

Lastpage

47

Abstract

Cell image segmentation is a necessary first step of many automated biomedical image-processing procedures. There certainly has been much research in the area. To this, a new method has been added, which automatically extracts cells from microscopic imagery, and does so in two phases. Phase 1 uses iterated thresholding to identify and mark foreground objects or `blobs´ with an overall accuracy of >97%. Phase 2 of the method uses a novel genetic algorithms-based ellipse detection algorithm to identify cells, quickly and reliably. The mechanism, as a whole, has an accuracy rate >96% and takes <1 min (given our specific hardware configuration) to operate on a microscopic image

Keywords

cellular biophysics; feature extraction; genetic algorithms; image segmentation; medical image processing; automated biomedical image processing; automatic cell image segmentation; ellipse detection; ellipse detection algorithm; genetic algorithm; microscopic cell image extraction;

fLanguage

English

Journal_Title

Image Processing, IET

Publisher

iet

ISSN

1751-9659

Type

jour

DOI

10.1049/iet-ipr:20045262

Filename

4149695