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Title: Quasi-supervised learning on DNA regions in colon cancer histology slides
Authors: Köktürk, Başak Esin
Karaçalı, Bilge
Keywords: Quasi-supervised learning
mathematical morphology
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)
ISBN: 978-1-4673-5563-6
ISSN: 2165-0608
Appears in Collections:WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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