A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography.
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.
Dettori
Lucia
L
Intelligent Multimedia Processing Laboratory, School of Computer Science, Telecommunications, and Information Systems, DePaul University, 243 S. Wabash Avenue, Chicago, IL 60604, USA. ldettori@cs.depaul.edu
Semler
Lindsay
L
D000465:Algorithms; D001185:Artificial Intelligence; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008432:Mathematical Computing; D012984:Software; D014057:Tomography, X-Ray Computed / Q000379:methods*