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|Title:||Quasi-supervised learning on DNA regions in colon cancer histology slides||Authors:||Köktürk, Başak Esin
|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
|Appears in Collections:||WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection|
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