An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells

Lu, Zhi, Carneiro, Gustavo and Bradley, Andrew P (2015) An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Transactions on Image Processing, 24 4: 1261-1272. doi:10.1109/TIP.2015.2389619


Author Lu, Zhi
Carneiro, Gustavo
Bradley, Andrew P
Title An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
1941-0042
Publication date 2015-04
Year available 2015
Sub-type Article (original research)
DOI 10.1109/TIP.2015.2389619
Open Access Status
Volume 24
Issue 4
Start page 1261
End page 1272
Total pages 12
Place of publication Piscataway, United States
Publisher Institute of Electrical and Electronics Engineers
Collection year 2016
Language eng
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.
Keyword Overlapping cell segmentation
Pap smear image analysis
Level set method
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 9 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 13 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 24 Mar 2015, 00:30:21 EST by System User on behalf of Scholarly Communication and Digitisation Service