Adaptive discriminant wavelet packet transform and local binary patterns for meningioma subtype classification
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 9783540859901Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/978-3-540-85990-1_24
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|
|Book Title:||Medical image computing and computer-assisted intervention – MICCAI 2008|
|Editor:||Metaxas, D. and Axel, L. and Fichtinger, G. and Szekely, G.|
|Page Range:||pp. 196-204|
|Status:||Not Peer Reviewed|
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