Joint shape matching for overlapping cytoplasm segmentation in cervical smear images
Publication Type
Conference Proceeding Article
Publication Date
4-2019
Abstract
We present a novel and effective approach to segmenting overlapping cytoplasm of cells in cervical smear images. Instead of simply combining individual cytoplasm shape information with the intensity or color information for the segmentation, our approach aims at simultaneously matching an accurate shape template for each cytoplasm in a whole clump. There are two main technical contributions. First, we present a novel shape similarity measure that supports shape template matching without clump splitting, allowing us to leverage more shape information, not only from the cytoplasm itself but also from the whole clump. Second, we propose an effective objective function for joint shape template matching based on our shape similarity measure; unlike individual matching, our method is able to exploit more shape constraints. We extensively evaluate our method on two typical cervical smear data sets. Experimental results show that our method outperforms the state-of-the-art methods in term of segmentation accuracy.
Keywords
Cervical smear images, Color information, Effective approaches, Objective function, ; Segmentation accuracy, Shape information, State-of-the-art methods, Technical contribution
Discipline
Databases and Information Systems
Research Areas
Information Systems and Management
Publication
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, Venice, Italy, 2019 April 8-11
First Page
191
Last Page
194
ISBN
9781538636411
Identifier
10.1109/ISBI.2019.8759259
Publisher
IEEE
City or Country
New Jersey
Citation
SONG, Youyi; QIN, Jing; LEI, Baiying; HE, Shengfeng; and CHOI, Kup-Sze.
Joint shape matching for overlapping cytoplasm segmentation in cervical smear images. (2019). Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, Venice, Italy, 2019 April 8-11. 191-194.
Available at: https://ink.library.smu.edu.sg/sis_research/8563
Additional URL
http://doi.org/10.1109/ISBI.2019.8759259