Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/95400
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells
Author: Lu, Z.
Carneiro, G.
Bradley, A.
Citation: IEEE Transactions on Image Processing, 2015; 24(4):1261-1272
Publisher: IEEE
Issue Date: 2015
ISSN: 1057-7149
1941-0042
Statement of
Responsibility: 
Zhi Lu, Gustavo Carneiro, and Andrew P. Bradley
Abstract: 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 <0.2. Moreover, if the clump consists of three or fewer cells, then our methodology can successfully segment individual cells even when the overlap is ∼0.5. We also evaluate our approach quantitatively and qualitatively on a set of 16 extended depth of field images, where we are able to segment a total of 645 cells, of which only ∼10% are free-lying. Finally, we demonstrate that our method of cell nuclei segmentation is competitive when compared with the current state of the art.
Keywords: Humans; Image Interpretation, Computer-Assisted; Microscopy; Image Enhancement; Sensitivity and Specificity; Reproducibility of Results; Algorithms; Pattern Recognition, Automated; Uterine Cervical Neoplasms; Female; Cell Tracking; Papanicolaou Test; Machine Learning
Rights: © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030024478
DOI: 10.1109/TIP.2015.2389619
Grant ID: http://purl.org/au-research/grants/arc/DP140102794
http://purl.org/au-research/grants/arc/FT110100623
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
RA_hdl_95400.pdfRestricted Access6.9 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.