Carcinoma cell line discrimination in microscopic images using unbalanced wavelets
2012 46th Annual Conference on Information Sciences and Systems, CISS 2012
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28139
Cancer 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.
- Conference Paper 2294