An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells

In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells. This problem is notoriously difficult because of the degree of overlap among cells, the poor contrast of cell cytoplasm and the presence of mucus, blood, and inflam...

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Bibliographic Details
Published in:IEEE transactions on image processing 2015-04, Vol.24 (4), p.1261-1272
Main Authors: Zhi Lu, Carneiro, Gustavo, Bradley, Andrew P.
Format: Article
Language:English
Subjects:
Algorithms
Cell Tracking - methods
Cells (biology)
Female
Humans
Image edge detection
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image segmentation
Level set
Level set method
Machine Learning
Microscopy - methods
Optimization
Overlapping cell segmentation
Pap smear image analysis
Papanicolaou Test - methods
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Shape
Uterine Cervical Neoplasms - pathology
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Summary:In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells. This problem is notoriously difficult because of the degree of overlap among cells, the poor contrast of cell cytoplasm and the presence of mucus, blood, and inflammatory cells. Our methodology addresses these issues by utilizing a joint optimization of multiple level set functions, where each function represents a cell within a clump, that have both unary (intracell) and pairwise (intercell) constraints. The unary constraints are based on contour length, edge strength, and cell shape, while the pairwise constraint is computed based on the area of the overlapping regions. In this way, our methodology enables the analysis of nuclei and cytoplasm from both free-lying and overlapping cells. We provide a systematic evaluation of our methodology using a database of over 900 images generated by synthetically overlapping images of free-lying cervical cells, where the number of cells within a clump is varied from 2 to 10 and the overlap coefficient between pairs of cells from 0.1 to 0.5. This quantitative assessment demonstrates that our methodology can successfully segment clumps of up to 10 cells, provided the overlap between pairs of cells is
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2015.2389619