A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography

Authors:
Lucia Dettori;Lindsay Semler
Affiliations:
Intelligent Multimedia Processing Laboratory, School of Computer Science, Telecommunications, and Information Systems, DePaul University, 243 S. Wabash Avenue, Chicago, IL 60604, USA;Intelligent Multimedia Processing Laboratory, School of Computer Science, Telecommunications, and Information Systems, DePaul University, 243 S. Wabash Avenue, Chicago, IL 60604, USA
Venue:
Computers in Biology and Medicine
Year:
2007

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Abstract

The research presented in this article is aimed at the development of an automated imaging system for classification of normal tissues in medical images obtained from computed tomography (CT) scans. This article focuses on comparing the discriminating power of several multi-resolution texture analysis techniques using wavelet, ridgelet, and curvelet-based texture descriptors. The approach consists of two steps: automatic extraction of the most discriminative texture features of regions of interest and creation of a classifier that automatically identifies the various tissues. The algorithms are extensively tested and results are compared with standard texture classification algorithms. Tests indicate that using curvelet-based texture features significantly improves the classification of normal tissues in CT scans.