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.