DocumentCode :
2290575
Title :
Spatiotemporal mitosis event detection in time-lapse phase contrast microscopy image sequences
Author :
Liu, Anan ; Li, Kang ; Kanade, Takeo
Author_Institution :
Sch. of Electron. Eng., Tianjin Univ., Tianjin, China
fYear :
2010
fDate :
19-23 July 2010
Firstpage :
161
Lastpage :
166
Abstract :
High-throughput automated analysis of cell population behaviors in vitro is of great importance to biological research. In particular, automated quantification of cellular mitosis in time-lapse microscopy video is useful for multiple applications such as tissue engineering, cancer research, and developmental biology. Accurate localization and counting of mitosis are challenging since cells undergo drastic morphological and appearance changes during mitosis. To tackle this challenge, we propose a fully-automated detection method for cells imaged with phase contrast microscopy. The method consists of three stages: image preconditioning, spatiotemporal volume extraction and SVM - based mitosis event detection. First, the input images are transformed based on physics of phase contrast image formation such that potential mitosis regions are assigned high values. Second, volumetric region grow was performed on the transformed images to extract candidate mitosis regions. Third, mitosis events are detected in the candidates using a Support Vector Machine (SVM) classifier. The proposed method does not depend on empirical parameters, ad hoc image processing, or explicit cell tracking; and can be straightforwardly adapted to different cell types. It was validated with 10 image sequences consisting of 8000 images, and achieved excellent performance with 90.6% average precision and 95.6% average recall.
Keywords :
biological tissues; biology computing; cancer; feature extraction; image classification; image sequences; microscopy; support vector machines; tissue engineering; biological research; cancer research; cell population behaviors; cellular mitosis quantification; developmental biology; fully-automated detection method; high-throughput automated analysis; image preconditioning; phase contrast image formation; spatiotemporal mitosis event detection; spatiotemporal volume extraction; support vector machine classifier; time-lapse microscopy video; time-lapse phase contrast microscopy image sequences; tissue engineering; Event detection; Feature extraction; Image sequences; Microscopy; Pixel; Spatiotemporal phenomena; Support vector machines; Image Preconditioning; Mitosis; Phase Contrast Microscopy; Volumetric Region Grow;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo (ICME), 2010 IEEE International Conference on
Conference_Location :
Suntec City
ISSN :
1945-7871
Print_ISBN :
978-1-4244-7491-2
Type :
conf
DOI :
10.1109/ICME.2010.5583299
Filename :
5583299
Link To Document :
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