Keskin, FurkanSuhre, AlexanderErşahin, Tüli,Çetin Atalay, RengülÇetin, A. Enis2016-02-082016-02-082012-03http://hdl.handle.net/11693/28139Date of Conference: 21-23 March 2012Conference name: 46th Annual Conference on Information Sciences and Systems (CISS), 2012Cancer 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.EnglishCarcinoma cell lineCorrelation descriptorMicroscopic image processingUnbalanced waveletCancer cell linesCarcinoma cell linesCarcinoma cellsCell linesClassical correlationConventional methodsData setsDescriptorsLiver cancer cellsMicroscopic imageMicroscopic image processingMultiple orientationsRadial basis functionsTextural featureUnbalanced waveletWavelet transform coefficientsCellsCorrelation methodsImage processingInformation sciencePixelsRadial basis function networksSupport vector machinesCell cultureConference Paper10.1109/CISS.2012.6310726