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Proceedings Paper

Graph cut and image intensity-based splitting improves nuclei segmentation in high-content screening
Author(s): Muhammad Farhan; Pekka Ruusuvuori; Mario Emmenlauer; Pauli Rämö; Olli Yli-Harja; Christoph Dehio
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Paper Abstract

Quantification of phenotypes in high-content screening experiments depends on the accuracy of single cell analysis. In such analysis workflows, cell nuclei segmentation is typically the first step and is followed by cell body segmentation, feature extraction, and subsequent data analysis workflows. Therefore, it is of utmost importance that the first steps of high-content analysis are done accurately in order to guarantee correctness of the final analysis results. In this paper, we present a novel cell nuclei image segmentation framework which exploits robustness of graph cut to obtain initial segmentation for image intensity-based clump splitting method to deliver the accurate overall segmentation. By using quantitative benchmarks and qualitative comparison with real images from high-content screening experiments with complicated multinucleate cells, we show that our method outperforms other state-of-the-art nuclei segmentation methods. Moreover, we provide a modular and easy-to-use implementation of the method for a widely used platform.

Paper Details

Date Published: 19 February 2013
PDF: 10 pages
Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 86550F (19 February 2013); doi: 10.1117/12.2003243
Show Author Affiliations
Muhammad Farhan, Tampere Univ. of Technology (Finland)
Pekka Ruusuvuori, Tampere Univ. of Technology (Finland)
Mario Emmenlauer, Univ. Basel (Switzerland)
Pauli Rämö, Univ. Basel (Switzerland)
Olli Yli-Harja, Tampere Univ. of Technology (Finland)
Christoph Dehio, Univ. Basel (Switzerland)


Published in SPIE Proceedings Vol. 8655:
Image Processing: Algorithms and Systems XI
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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