Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48806
Title: Automated segmentation of colon gland using histology images
Authors: Banwari, Anamika
Sengar, Namita
Dutta, Malay Kishore
Travieso, Carlos M. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Cancer-Detection
Histology Images Analysis
Stain Colon Biopsy
Colorectal Cancer
Colon Gland Segmentation
Image Segmentaion
Issue Date: 2017
Journal: 2016 9th International Conference on Contemporary Computing, IC3 2016
Conference: 9th International Conference on Contemporary Computing, IC3 2016 
Abstract: This paper represents an automated methodology for segmentation of colon glands using histology images. The manifestations of colorectal cancer under microscope has always been challenging as staining and sectioning leads to variation in tissue specimen, which causes conflict in gland appearance. Gland segmentation and classification is very important for the automation of the system. The presented methodology automatically segments the colon gland tissues by using intensity based thresholding which makes this methodology efficient. Unlike other segmentation methods, this methodology is entirely automated and quantifies lumen and epithelial cells only in the region of interest, which makes this method computationally efficient. This methodology is efficient for calculation of number of glands as well as for segmentation of gland area and achieves overall 93.76% accuracy for both.
URI: http://hdl.handle.net/10553/48806
ISBN: 9781509032518
ISSN: 2572-6110
DOI: 10.1109/IC3.2016.7880223
Source: 2016 9th International Conference on Contemporary Computing, IC3 2016 (7880223)
Appears in Collections:Actas de congresos
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