NDLI: Texture Based Classification and Segmentation of Tissues Using DT-CWT Feature Extraction Methods

Content Provider	IEEE Xplore Digital Library
Author	Aydogan, D.B. Hannula, M. Arola, T. Hyttinen, J. Dastidar, P.
Copyright Year	2008
Description	Author affiliation: Dept. of Biomed. Eng., Tampere Univ. of Technol., Tampere (Aydogan, D.B.; Hannula, M.; Arola, T.; Hyttinen, J.)
Abstract	In this study, four different dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and compared to segment and classify tissues. Methods that are proposed in this study are based on local energy calculations of sub-bands. Two of the methods use rotation variant texture features and the other two use rotation invariant features. The methods are tested on two texture compositions from the Brodatz texture database and two actual magnetic resonance (MR) images. Results show that there is not a significant difference between using rotation variant or invariant features. On the other hand, for the same Brodatz textures, all DT-CWT based feature extraction methods are competitive with other filtering approaches.
Starting Page	614
Ending Page	619
File Size	1446039
Page Count	6
File Format	PDF
ISBN	9780769531656
ISSN	10637125
DOI	10.1109/CBMS.2008.46
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-17
Publisher Place	Finland
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Discrete wavelet transforms Biomedical imaging Image segmentation Testing Image databases Spatial databases Magnetic resonance Computed tomography Biomedical computing
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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