Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9822
Title: Histoloji görüntülerinde kanserli desenlerin yarı güdümlü öğrenme yöntemiyle tam otomatik sınıflandırılması
Other Titles: Automated classification of cancerous textures in histology images using quasi-supervised learning algorithm
Authors: Önder, Devrim
Sarıoğlu, Sülen
Karaçalı, Bilge
Keywords: Co-occurrence matrice
Quasi-supervised statistical learning
Texture classification
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Abstract: The aim of this work is to perform automated texture classification of histology slide images in health and cancerous conditions using quasi-supervised statistical learning method. Tissue images were acquired from histological slides of human colon and were seperated into two groups in terms of normal and disease conditions. Texture feature vectors corresponding to tissue segments of each image were calculated using co-occurrence matrices. Different texture regions were determined by the quasi-supervised statistical learning method using texture features of normal and cancerous groups. ©2010 IEEE.
URI: https://doi.org/10.1109/BIYOMUT.2010.5479863
https://hdl.handle.net/11147/9822
ISBN: 978-142446382-4
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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