A Two-Phase Segmentation of Cell Nuclei Using Fast Level Set-Like Algorithms

Authors:
Martin Maška;Ondřej Daněk;Carlos Ortiz-De-Solórzano;Arrate Muñoz-Barrutia;Michal Kozubek;Ignacio Fernández García
Affiliations:
Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic;Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic;Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain;Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain;Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czech Republic;Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain
Venue:
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Year:
2009

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Abstract

An accurate localization of a cell nucleus boundary is inevitable for any further quantitative analysis of various subnuclear structures within the cell nucleus. In this paper, we present a novel approach to the cell nucleus segmentation in fluorescence microscope images exploiting the level set framework. The proposed method works in two phases. In the first phase, the image foreground is separated from the background using a fast level set-like algorithm by Nilsson and Heyden [1]. A binary mask of isolated cell nuclei as well as their clusters is obtained as a result of the first phase. A fast topology-preserving level set-like algorithm by Maška and Matula [2] is applied in the second phase to delineate individual cell nuclei within the clusters. The potential of the new method is demonstrated on images of DAPI-stained nuclei of a lung cancer cell line A549 and promyelocytic leukemia cell line HL60.