TY - GEN
T1 - Spatial Shape Constrained Fuzzy C-Means (FCM) Clustering for Nucleus Segmentation in Pap Smear Images
AU - Saha, Ratna
AU - Bajger, Mariusz
AU - Lee, Gobert
PY - 2016/12/22
Y1 - 2016/12/22
N2 - Precise segmentation of Pap smear cell nucleus is crucial for early diagnosis of cervical cancer. This task is particularly challenging because of cell overlapping, inconsistent staining, poor contrast and other imaging artifacts. In this study, a novel method is proposed to segment cell nucleus from overlapping Pap smear cell images. The proposed technique introduces a circular shape function (CSF) to increase the robustness of Pap cell nucleus segmentation using fuzzy c-means clustering. CSF imposes a shape constrain over the formed clusters, while improves the boundary of the nucleus. The shape function helps to differentiate the pixels having similar intensity value but located in different spatial regions. The method is evaluated using Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 dataset and compared with the traditional FCM clustering and recently published state-of-the-art methods. Both qualitative and quantitative measures indicate that the new technique performs favorably with others.
AB - Precise segmentation of Pap smear cell nucleus is crucial for early diagnosis of cervical cancer. This task is particularly challenging because of cell overlapping, inconsistent staining, poor contrast and other imaging artifacts. In this study, a novel method is proposed to segment cell nucleus from overlapping Pap smear cell images. The proposed technique introduces a circular shape function (CSF) to increase the robustness of Pap cell nucleus segmentation using fuzzy c-means clustering. CSF imposes a shape constrain over the formed clusters, while improves the boundary of the nucleus. The shape function helps to differentiate the pixels having similar intensity value but located in different spatial regions. The method is evaluated using Overlapping Cervical Cytology Image Segmentation Challenge - ISBI 2014 dataset and compared with the traditional FCM clustering and recently published state-of-the-art methods. Both qualitative and quantitative measures indicate that the new technique performs favorably with others.
UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7797086
UR - http://www.scopus.com/inward/record.url?scp=85011026179&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2016.7797086
DO - 10.1109/DICTA.2016.7797086
M3 - Conference contribution
SN - 978-1-5090-2896-2
T3 - 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016
BT - 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
A2 - Liew, Alan Wee-Chung
A2 - Zhou, Jun
A2 - Gao, Yongsheng
A2 - Wang, Zhiyong
A2 - Fookes, Clinton
A2 - Lovell, Brian
A2 - Blumenstein, Michael
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway, NJ
T2 - 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Y2 - 30 November 2016
ER -