Mixture of Learners for Cancer Stem Cell Detection Using CD13 and H&E Stained Images

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2016-03-03

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OĞUZ, OĞUZHAN
Akbas, Cem Emre
Mallah, Maen
TAŞDEMİR, KASIM
Guzelcan, Ece Akhan
Muenzenmayer, Christian
Wittenberg, Thomas
ÜNER, AYŞEGÜL
ÇETİN, AHMET ENİS
Atalay, Rengül

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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&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&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&E stained microscopic tissue images.

Subject Keywords

Cancer Stem Cell Detection, CD13 Stain, H&E Stain, Region Covariance Descriptor, Region Codifference Descriptor, Online Learning, 1-D SIFT, Eigenface

URI

https://hdl.handle.net/11511/32119

DOI

https://doi.org/10.1117/12.2216113

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Graduate School of Informatics, Conference / Seminar

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Citation Formats

O. OĞUZ et al., “Mixture of Learners for Cancer Stem Cell Detection Using CD13 and H&E Stained Images,” 2016, vol. 9791, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/32119.