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Automated Nucleus and Cytoplasm Segmentation of Overlapping Cervical Cells
Zhi Lu1, Gustavo Carneiro2, and Andrew P. Bradley3
1Department of Computer Science, City University of Hong Kong, China
2ACVT, The University of Adelaide, Australia
3School of Information Technology & Electrical Engineering, The University of Queensland, Australia
Abstract. In this paper we describe an algorithm for accurately segmenting the individual cytoplasm and nuclei from a clump of overlapping cervical cells. Current methods cannot undertake such a complete segmentation due to the challenges involved in delineating cells with severe overlap and poor contrast. Our approach initially performs a scene segmentation to highlight both free-lying cells, cell clumps and their nuclei. Then cell segmentation is performed using a joint level set optimization on all detected nuclei and cytoplasm pairs. This optimisation is constrained by the length and area of each cell, a prior on cell shape, the amount of cell overlap and the expected gray values within the overlapping regions. We present quantitative nuclei detection and cell segmentation results on a database of synthetically overlapped cell images constructed from real images of free-lying cervical cells. We also perform a qualitative assessment of complete fields of view containing multiple cells and cell clumps.
Keywords: Overlapping cell segmentation, Pap smear image analysis
LNCS 8149, p. 452 ff.
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© Springer-Verlag Berlin Heidelberg 2013