loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ravali Edulapuram 1 ; R. Joe Stanley 1 ; Rodney Long 2 ; Sameer Antani 2 ; George Thoma 2 ; Rosemary Zuna 3 ; William V. Stoecker 4 and Jason Hagerty 5

Affiliations: 1 Missouri University of Science and Technology, United States ; 2 Lister Hill Center for Biomedical Communications, National Library of Medicine and National Institutes of Health, United States ; 3 University of Oklahoma and University of Oklahoma Health Sciences Center, United States ; 4 Stoecker & Associates, United States ; 5 Missouri University of Science and Technology and Stoecker & Associates, United States

Keyword(s): Nuclei Segmentation, Level Set Method, Active Contours, Fuzzy C-means Clustering, Cervical Cancer, Epithelium, Image Processing.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Image Enhancement and Restoration ; Image Formation and Preprocessing ; Segmentation and Grouping

Abstract: Digitized histology images are analyzed by expert pathologists in one of several approaches to assess pre-cervical cancer conditions such as cervical intraepithelial neoplasia (CIN). Many image analysis studies focus on detection of nuclei features to classify the epithelium into the CIN grades. The current study focuses on nuclei segmentation based on level set active contour segmentation and fuzzy c-means clustering methods. Logical operations applied to morphological post-processing operations are used to smooth the image and to remove non-nuclei objects. On a 71-image dataset of digitized histology images (where the ground truth is the epithelial mask which helps in eliminating the non epithelial regions), the algorithm achieved an overall nuclei segmentation accuracy of 96.47%. We propose a simplified fuzzy spatial cost function that may be generally applicable for any n-class clustering problem of spatially distributed objects.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.200.122.214

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Edulapuram, R.; Stanley, R.; Long, R.; Antani, S.; Thoma, G.; Zuna, R.; Stoecker, W. and Hagerty, J. (2017). Nuclei Segmentation using a Level Set Active Contour Method and Spatial Fuzzy C-means Clustering. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP; ISBN 978-989-758-225-7; ISSN 2184-4321, SciTePress, pages 195-202. DOI: 10.5220/0006136201950202

@conference{visapp17,
author={Ravali Edulapuram. and R. Joe Stanley. and Rodney Long. and Sameer Antani. and George Thoma. and Rosemary Zuna. and William V. Stoecker. and Jason Hagerty.},
title={Nuclei Segmentation using a Level Set Active Contour Method and Spatial Fuzzy C-means Clustering},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP},
year={2017},
pages={195-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006136201950202},
isbn={978-989-758-225-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 4: VISAPP
TI - Nuclei Segmentation using a Level Set Active Contour Method and Spatial Fuzzy C-means Clustering
SN - 978-989-758-225-7
IS - 2184-4321
AU - Edulapuram, R.
AU - Stanley, R.
AU - Long, R.
AU - Antani, S.
AU - Thoma, G.
AU - Zuna, R.
AU - Stoecker, W.
AU - Hagerty, J.
PY - 2017
SP - 195
EP - 202
DO - 10.5220/0006136201950202
PB - SciTePress