Carcinoma cell line discrimination in microscopic images using unbalanced wavelets

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.contributor.authorKeskin, Furkanen_US
dc.contributor.authorSuhre, Alexanderen_US
dc.contributor.authorErşahin, Tüli,en_US
dc.contributor.authorÇetin Atalay, Rengülen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.coverage.spatialPrinceton, NJ, USA
dc.date.accessioned2016-02-08T12:12:12Z
dc.date.available2016-02-08T12:12:12Z
dc.date.issued2012-03en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.descriptionDate of Conference: 21-23 March 2012
dc.descriptionConference name: 46th Annual Conference on Information Sciences and Systems (CISS), 2012
dc.description.abstractCancer cell lines are widely used for research purposes in laboratories all over the world. In this paper, we present a novel method for cancer cell line image classification, which is very costly by conventional methods. 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 randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a correlation descriptor utilizing the fractional unbalanced wavelet transform coefficients and several morphological attributes as pixel features is computed. Directionally selective textural features are preferred primarily because of their ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines. A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel is employed for final classification. Over a dataset of 280 images, we achieved an accuracy of 88.2%, which outperforms the classical correlation based methods. © 2012 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T12:12:12Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2012en
dc.identifier.doi10.1109/CISS.2012.6310726en_US
dc.identifier.urihttp://hdl.handle.net/11693/28139
dc.language.isoEnglishen_US
dc.publisherIEEE
dc.relation.isversionofhttp://dx.doi.org/10.1109/CISS.2012.6310726en_US
dc.source.title46th Annual Conference on Information Sciences and Systems, CISS 2012en_US
dc.subjectCarcinoma cell lineen_US
dc.subjectCorrelation descriptoren_US
dc.subjectMicroscopic image processingen_US
dc.subjectUnbalanced waveleten_US
dc.subjectCancer cell linesen_US
dc.subjectCarcinoma cell linesen_US
dc.subjectCarcinoma cellsen_US
dc.subjectCell linesen_US
dc.subjectClassical correlationen_US
dc.subjectConventional methodsen_US
dc.subjectData setsen_US
dc.subjectDescriptorsen_US
dc.subjectLiver cancer cellsen_US
dc.subjectMicroscopic imageen_US
dc.subjectMicroscopic image processingen_US
dc.subjectMultiple orientationsen_US
dc.subjectRadial basis functionsen_US
dc.subjectTextural featureen_US
dc.subjectUnbalanced waveleten_US
dc.subjectWavelet transform coefficientsen_US
dc.subjectCellsen_US
dc.subjectCorrelation methodsen_US
dc.subjectImage processingen_US
dc.subjectInformation scienceen_US
dc.subjectPixelsen_US
dc.subjectRadial basis function networksen_US
dc.subjectSupport vector machinesen_US
dc.subjectCell cultureen_US
dc.titleCarcinoma cell line discrimination in microscopic images using unbalanced waveletsen_US
dc.typeConference Paperen_US

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