Paper Info
Title
A comparison of wavelet-based and ridgelet-based texture classification of tissues in computed tomography
Abstract
The research presented in this article is aimed at developing an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The article focuses on using multi-resolution texture analysis, specifically: the Haar wavelet, Daubechies wavelet, Coiflet wavelet, and the ridgelet. The algorithm 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 classification step is implemented using a cross-validation Classification and Regression Tree approach. A comparison of wavelet-based and ridgelet-based algorithms is presented. Tests on a large set of chest and abdomen CT images indicate that, among the three wavelet-based algorithms, the one using texture features derived from the Haar wavelet transform clearly outperforms the one based on Daubechies and Coiflet transform. The tests also show that the ridgelet-based algorithm is significantly more effective and that texture features based on the ridgelet transform are better suited for texture classification in CT medical images.
Year
DOI
Venue
2006
10.1007/978-3-540-75274-5_16
ADVANCES IN COMPUTER GRAPHICS AND COMPUTER VISION
Keywords
Field
DocType
multi-resolution analysis,texture classification,wavelet,ridgelet,Computed Tomography
Computer vision,Decision tree,Coiflet,Pattern recognition,Computer science,Image texture,Artificial intelligence,Daubechies wavelet,Haar wavelet,Classifier (linguistics),Discriminative model,Wavelet
Conference
Volume
ISSN
Citations 
4
1865-0929
6
PageRank 
References 
Authors
0.76
5
2
Name
Order
Citations
PageRank
Lindsay Semler1714.87
Lucia Dettori211813.13