DocumentCode :
2804270
Title :
Contour Extraction of Drosophila Embryos
Author :
Li, Qi ; Kambhamettu, Chandra
Author_Institution :
Dept. of Math & CS, Western Kentucky Univ., Bowling Green, KY, USA
fYear :
2009
fDate :
2-4 Nov. 2009
Firstpage :
33
Lastpage :
40
Abstract :
Contour extraction of Drosophila (fruit fly) embryos is an important step to build a computational system for matching expression pattern of embryonic images to assist the discovery of the nature of genes. Automatic contour extraction of embryos is challenging due to severe image variations, including i) the size, orientation, shape and appearance of an embryo of interest; ii) the neighboring context of an embryo of interest (such as non-touching and touching neighboring embryos); and iii) illumination circumstance. In this paper, we propose an automatic framework for contour extraction of the embryo of interest in an embryonic image. The proposed framework contains three components. Its first component applies a mixture model of quadratic curves, with statistical features, to initialize the contour of the embryo of interest. An efficient method based on imbalanced image points is proposed to compute model parameters. The second component applies active contour model to refine embryo contours. The third component applies eigen-shape modeling to smooth jaggy contours caused by blur embryo boundaries. We test the proposed framework on a dataset of 8000 embryonic images, and achieve promising accuracy (88%) that is substantially higher than the-state-of-the-art results.
Keywords :
biology computing; edge detection; eigenvalues and eigenfunctions; genetics; image matching; Drosophila embryos; contour extraction; eigenshape modeling; expression pattern; genes; image matching; imbalanced image points; quadratic curves; Active contours; Artificial intelligence; Data mining; Embryo; Gene expression; Image edge detection; Image segmentation; Lighting; Pixel; Shape; Contour extraction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on
Conference_Location :
Newark, NJ
ISSN :
1082-3409
Print_ISBN :
978-1-4244-5619-2
Electronic_ISBN :
1082-3409
Type :
conf
DOI :
10.1109/ICTAI.2009.44
Filename :
5362619
Link To Document :
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