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http://dx.doi.org/10.5392/JKCA.2010.10.4.019

Automatic Segmentation of Cellular Images for High-Throughput Genome-Wide RNA Interference Screening  

Han, Chan-Hee (한밭대학교 정보통신전문대학원)
Song, In-Hwan (한밭대학교 정보통신전문대학원)
Lee, Si-Woong (한밭대학교 정보통신전문대학원)
Publication Information
The Journal of the Korea Contents Association / v.10, no.4, 2010 , pp. 19-27 More about this Journal
Abstract
In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.
Keywords
High-Throughput Genome-Wide RNA Interference Screening; Nuclei Extraction; Cell Segmentation;
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