DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD

Date

2015-10-30

Author

Oguz, Oguzhan
Muenzenmayer, Christian
Wittenberg, Thomas
ÜNER, AYŞEGÜL
ÇETİN, AHMET ENİS
Atalay, Rengül

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

222
views

0
downloads

This paper presents a cancer stem cell detection method using region covariance and codifference method. It focuses on detection of Cancer Stem Cell (CSC) in microscopic images which are stained with CD13 marker. Features of CSC images are extracted by using both covariance method and its multiplication free version codifference method and these features are fed into a Support Vector Machine (SVM) for classification. Experimental results are presented.

Subject Keywords

Cancer cell detection, Stem cell recognition, Covariance features, Codifference features, Support vector machines, F1 score

URI

https://hdl.handle.net/11511/54483

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

Classification of Human Carcinoma Cells Using Multispectral Imagery Çinar, Umut; Çetin, Yasemin; Atalay, Rengül; Cetin, Enis (2016-03-03) In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. Th...
Characterization and identification of human mesenchymal stem cells at molecular level Aksoy, Ceren; Severcan, Feride; Çetinkaya, Duygu Uçkan; Department of Biotechnology (2012) Bone marrow mesenchymal stem cells (BM-MSCs) are pluripotent cells that can differentiate into a variety of non-hematopoietic tissues. They also maintain healthy heamatopoiesis by providing supportive cellular microenvironment into BM. In this thesis, MSCs were characterized in terms of their morphological, immunophenotypical and differentiation properties. Then, they were examined by attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy together with hierarchical clustering, and FT...
Mixture of Learners for Cancer Stem Cell Detection Using CD13 and H&E Stained Images OĞUZ, OĞUZHAN; Akbas, Cem Emre; Mallah, Maen; TAŞDEMİR, KASIM; Guzelcan, Ece Akhan; Muenzenmayer, Christian; Wittenberg, Thomas; ÜNER, AYŞEGÜL; ÇETİN, AHMET ENİS; Atalay, Rengül (2016-03-03) 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 tissue...
Detection of imatinib resistance in K562 leukemia cells by 3D-electrode contactless dielectrophoresis Demircan, Y.; Koyuncuoglu, A.; Erdem, Murat; Ozgur, E.; Gündüz, Ufuk; Külah, Haluk (2013-12-01) This paper presents the trapping of imatinib resistant K562 (human chronic myelogenic leukemia (CML), K562/IMA) cells with 3D-electrode contactless dielectrophoresis (DEP). 3D electrodes are isolated from the solution by means of uniform thin parylene layer (~0.3μm), to eliminate the Joule heating, electrolysis, and cell damaging. 3D electrodes (extruded along the microchannel in z-axis) provide uniform distribution of DEP force along the channel height, improving the separation efficiency, significantly. I...
Modeling stochastic hybrid systems with memory with an application to immune response of cancer dynamics Gökgöz, Nurgül; Öktem, Hakan; Piazza, Carla; Department of Scientific Computing (2014) Dynamics of cancer involve some complex interactions like immune system responses. Many different models of immune response to tumor growth exist in the literature. Most of the available models are first principles models which have problems in determining the model parameters. For potential use in treatment planning, a model should be able to adopt to subject by subject variability and unknown factors. However, such an approach for a complicated problem like cancer dynamics has some drawbacks. First of all...

Citation Formats

O. Oguz, C. Muenzenmayer, T. Wittenberg, A. ÜNER, A. E. ÇETİN, and R. Atalay, “DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54483.