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Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification

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Qureshi, H., Sertel, O., Rajpoot, Nasir M. (Nasir Mahmood) and Wilson, Roland, 1949- (2008) Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification. In: Metaxas, D. and Axel, L. and Fichtinger, G. and Szekely, G., (eds.) Medical image computing and computer-assisted intervention – MICCAI 2008. Lecture Notes in Computer Science (5242). Springer-Verlag, pp. 196-204. ISBN 9783540859901

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Official URL: http://dx.doi.org/10.1007/978-3-540-85990-1_24

Abstract

The inherent complexity and non-homogeneity of texture makes classification in medical image analysis a challenging task. In this paper, we propose a combined approach for meningioma subtype classification using subband texture (macro) features and micro-texture features. These are captured using the Adaptive Wavelet Packet Transform (ADWPT) and Local Binary Patterns (LBPs), respectively. These two different textural features are combined together and used for classification. The effect of various dimensionality reduction techniques on classification performance is also investigated. We show that high classification accuracies can be achieved using ADWPT. Although LBP features do not provide higher overall classification accuracies than ADWPT, it manages to provide higher accuracy for a meningioma subtype that is difficult to classify otherwise.

Item Type: Book Item
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science > Computer Science
Series Name: Lecture Notes in Computer Science
Publisher: Springer-Verlag
ISBN: 9783540859901
Book Title: Medical image computing and computer-assisted intervention – MICCAI 2008
Editor: Metaxas, D. and Axel, L. and Fichtinger, G. and Szekely, G.
Date: 2008
Number: 5242
Page Range: pp. 196-204
Identification Number: 10.1007/978-3-540-85990-1_24
Status: Not Peer Reviewed
Publication Status: Published
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URI: http://wrap.warwick.ac.uk/id/eprint/47623

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