23 March 2016 Mixture of learners for cancer stem cell detection using CD13 and H and E stained images
Author Affiliations +
Abstract
In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oğuzhan Oğuz, Oğuzhan Oğuz, Cem Emre Akbaş, Cem Emre Akbaş, Maen Mallah, Maen Mallah, Kasım Taşdemir, Kasım Taşdemir, Ece Akhan Güzelcan, Ece Akhan Güzelcan, Christian Muenzenmayer, Christian Muenzenmayer, Thomas Wittenberg, Thomas Wittenberg, Ayşegül Üner, Ayşegül Üner, A. Enis Cetin, A. Enis Cetin, Rengül Çetin Atalay, Rengül Çetin Atalay, } "Mixture of learners for cancer stem cell detection using CD13 and H and E stained images", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910Y (23 March 2016); doi: 10.1117/12.2216113; https://doi.org/10.1117/12.2216113
PROCEEDINGS
16 PAGES


SHARE
Back to Top