Browsing by Author "Keskin, Furkan"

Now showing 1 - 4 of 4

Open Access
Carcinoma cell line discrimination in microscopic images using unbalanced wavelets
(IEEE, 2012-03) Keskin, Furkan; Suhre, Alexander; Erşahin, Tüli,; Çetin Atalay, Rengül; Çetin, A. Enis
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.
Open Access
Directionally selective fractional wavelet transform using a 2-d non-separable unbalanced lifting structure
(Springer, Berlin, Heidelberg, 2012) Keskin, Furkan; Çetin, A. Enis
In this paper, we extend the recently introduced concept of fractional wavelet transform to obtain directional subbands of an image. Fractional wavelet decomposition is based on two-channel unbalanced lifting structures whereby it is possible to decompose a given discrete-time signal x[n] sampled with period T into two sub-signals x 1[n] and x 2[n] whose average sampling periods are pT and qT, respectively. Fractions p and q are rational numbers satisfying the condition: 1/p+1/q=1. Filters used in the lifting structure are designed using the Lagrange interpolation formula. 2-d separable and non-separable extensions of the proposed fractional wavelet transform are developed. Using a non-separable unbalanced lifting structure, directional subimages for five different directions are obtained. © 2012 Springer-Verlag.
Open Access
Microscopic image classification via WT-based covariance descriptors using Kullback-Leibler distance
(IEEE, 2012) Keskin, Furkan; Çetin, A. Enis; Erşahin, Tülin; Çetin-Atalay, Rengül
In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. WT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L 2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method. © 2012 IEEE.
Open Access
Time-varying lifting structures for single-tree complexwavelet transform
(IEEE, 2012) Keskin, Furkan; Çetin, A. Enis
In this paper, we describe a single-tree complex wavelet transform method using time-varying lifting structures. In the dualtree complex wavelet transform (DT-CWT), two different filterbanks are executed in parallel to analyze a given input signal, which increases the amount of data after analysis. DT-CWT leads to a redundancy factor of 2 d for d-dimensional signals. In the proposed single-tree complex wavelet transform (ST-CWT) structure, filters of the lifting filterbank switch back and forth between the two analysis filters of the DT-CWT. This approach does not increase the amount of output data as it is a critically sampled transform and it has the desirable properties of DT-CWT such as shift-invariance and directional selectivity. The proposed filterbank is capable of constructing a complex wavelet-like transform. Examples are presented. © 2012 IEEE.