Paper Info
Title
Nuclei Segmentation Using A Level Set Active Contour Method And Spatial Fuzzy C-Means Clustering
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.
Year
DOI
Venue
2017
10.5220/0006136201950202
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 4
Keywords
Field
DocType
Nuclei Segmentation, Level Set Method, Active Contours, Fuzzy C-means Clustering, Cervical Cancer, Epithelium, Image Processing
Active contour model,Computer vision,Scale-space segmentation,Level set method,Computer science,Fuzzy logic,Level set,Image segmentation,Artificial intelligence,Fuzzy control system,Cluster analysis
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
8
Name
Order
Citations
PageRank
Ravali Edulapuram100.34
R. Joe Stanley29212.80
L. Rodney Long353456.98
Sameer Antani41402134.03
George R. Thoma51207132.81
Rosemary Zuna630.76
William V. Stoecker737142.00
Jason R. Hagerty8194.47