title: Automated nucleus and cytoplasm segmentation of overlapping cervical cells.
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.
authors: Department of Computer Science, City University of Hong Kong, China.
authors: Zhi
authors: Z
authors: Lu
authors: ACVT, The University of Adelaide, Australia.
authors: Gustavo
authors: G
authors: Carneiro
authors: School of Information Technology & Electrical Engineering, The University of Queensland, Australia.
authors: Andrew P
authors: AP
authors: Bradley
mesh_terms: D000465:Algorithms; D002467:Cell Nucleus / Q000648:ultrastructure*; D002584:Cervix Uteri / Q000166:cytology*; D003593:Cytoplasm / Q000648:ultrastructure*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique