Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classifi...

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Autores principales:	Furkan Keskin, Alexander Suhre, Kivanc Kose, Tulin Ersahin, A Enis Cetin, Rengul Cetin-Atalay
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c697cb1560cb4c549b36454835c89f65
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spelling	oai:doaj.org-article:c697cb1560cb4c549b36454835c89f652021-11-18T08:01:19ZImage classification of human carcinoma cells using complex wavelet-based covariance descriptors.1932-620310.1371/journal.pone.0052807https://doaj.org/article/c697cb1560cb4c549b36454835c89f652013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23341908/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.Furkan KeskinAlexander SuhreKivanc KoseTulin ErsahinA Enis CetinRengul Cetin-AtalayPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 1, p e52807 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Furkan Keskin Alexander Suhre Kivanc Kose Tulin Ersahin A Enis Cetin Rengul Cetin-Atalay Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
description	Cancer cell lines are widely used for research purposes in laboratories all over the world. Computer-assisted classification of cancer cells can alleviate the burden of manual labeling and help cancer research. In this paper, we present a novel computerized method for cancer cell line image classification. The aim is to automatically classify 14 different classes of cell lines including 7 classes of breast and 7 classes of liver cancer cells. Microscopic images containing irregular carcinoma cell patterns are represented by subwindows which correspond to foreground pixels. For each subwindow, a covariance descriptor utilizing the dual-tree complex wavelet transform (DT-[Formula: see text]WT) coefficients and several morphological attributes are computed. Directionally selective DT-[Formula: see text]WT feature parameters are preferred primarily because of their ability to characterize edges at multiple orientations which is the characteristic feature of carcinoma cell line images. A Support Vector Machine (SVM) classifier with radial basis function (RBF) kernel is employed for final classification. Over a dataset of 840 images, we achieve an accuracy above 98%, which outperforms the classical covariance-based methods. The proposed system can be used as a reliable decision maker for laboratory studies. Our tool provides an automated, time- and cost-efficient analysis of cancer cell morphology to classify different cancer cell lines using image-processing techniques, which can be used as an alternative to the costly short tandem repeat (STR) analysis. The data set used in this manuscript is available as supplementary material through http://signal.ee.bilkent.edu.tr/cancerCellLineClassificationSampleImages.html.
format	article
author	Furkan Keskin Alexander Suhre Kivanc Kose Tulin Ersahin A Enis Cetin Rengul Cetin-Atalay
author_facet	Furkan Keskin Alexander Suhre Kivanc Kose Tulin Ersahin A Enis Cetin Rengul Cetin-Atalay
author_sort	Furkan Keskin
title	Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
title_short	Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
title_full	Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
title_fullStr	Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
title_full_unstemmed	Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
title_sort	image classification of human carcinoma cells using complex wavelet-based covariance descriptors.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c697cb1560cb4c549b36454835c89f65
work_keys_str_mv	AT furkankeskin imageclassificationofhumancarcinomacellsusingcomplexwaveletbasedcovariancedescriptors AT alexandersuhre imageclassificationofhumancarcinomacellsusingcomplexwaveletbasedcovariancedescriptors AT kivanckose imageclassificationofhumancarcinomacellsusingcomplexwaveletbasedcovariancedescriptors AT tulinersahin imageclassificationofhumancarcinomacellsusingcomplexwaveletbasedcovariancedescriptors AT aeniscetin imageclassificationofhumancarcinomacellsusingcomplexwaveletbasedcovariancedescriptors AT rengulcetinatalay imageclassificationofhumancarcinomacellsusingcomplexwaveletbasedcovariancedescriptors
_version_	1718422632105246720

Image classification of human carcinoma cells using complex wavelet-based covariance descriptors.

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