Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9978
Title: Quasi-supervised learning on DNA regions in colon cancer histology slides
Authors: Köktürk, Başak Esin
Karaçalı, Bilge
Keywords: Quasi-supervised learning
mathematical morphology
segmentation
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers Inc.
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: The aim of this study, nuclei base automatic detection of cancerous regions via determination of DNA-rich regions in high definition histology images. In the study; DNA-rich regions were determined using k-means clustering and some mathematical morphology operations, the diseased regions were diagnosed using morphological characteristics via quasi-supervised learning. It's observed that quasi-supervised learning method successfully separates cancerous chromatin regions from others successfully with experiments of colon cross-section histology images.
Description: 21st Signal Processing and Communications Applications Conference (SIU)
URI: https://hdl.handle.net/11147/9978
ISBN: 978-1-4673-5563-6
978-1-4673-5562-9
ISSN: 2165-0608
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender

Page view(s)

46
checked on Aug 8, 2022

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.