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Proceedings Paper

Automatic cell segmentation in fluorescence images of confluent cell monolayers using multi-object geometric deformable model
Author(s): Zhen Yang; John A. Bogovic; Aaron Carass; Mao Ye; Peter C. Searson; Jerry L. Prince
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Paper Abstract

With the rapid development of microscopy for cell imaging, there is a strong and growing demand for image analysis software to quantitatively study cell morphology. Automatic cell segmentation is an important step in image analysis. Despite substantial progress, there is still a need to improve the accuracy, efficiency, and adaptability to different cell morphologies. In this paper, we propose a fully automatic method for segmenting cells in uorescence images of conuent cell monolayers. This method addresses several challenges through a combination of ideas. 1) It realizes a fully automatic segmentation process by first detecting the cell nuclei as initial seeds and then using a multi-object geometric deformable model (MGDM) for final segmentation. 2) To deal with different defects in the uorescence images, the cell junctions are enhanced by applying an orderstatistic filter and principal curvature based image operator. 3) The final segmentation using MGDM promotes robust and accurate segmentation results, and guarantees no overlaps and gaps between neighboring cells. The automatic segmentation results are compared with manually delineated cells, and the average Dice coefficient over all distinguishable cells is 0:88.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866904 (13 March 2013); doi: 10.1117/12.2006603
Show Author Affiliations
Zhen Yang, Johns Hopkins Univ. (United States)
John A. Bogovic, Johns Hopkins Univ. (United States)
Aaron Carass, Johns Hopkins Univ. (United States)
Mao Ye, Johns Hopkins Univ. (United States)
Peter C. Searson, Johns Hopkins Univ. (United States)
Jerry L. Prince, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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