DocumentCode :
770979
Title :
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
Link To Document :
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