Texture Classification in Lung CT Using Local Binary Patterns
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Abstract In this paper we propose to use local binary patterns (LBP) as features in a classification framework for classifying different texture patterns in lung computed tomography. Image intensity is included by means of the joint LBP and intensity histogram, and classification is performed using the k nearest neighbor classifier with histogram similarity as distance measure. The proposed method is evaluated on a set of 168 regions of interest comprising normal tissue and different emphysema patterns, and compared to a filter bank based on Gaussian derivatives. The joint LBP and intensity histogram, achieving a classification accuracy of 95.2%, shows superior performance to using the common approach of taking moments of the filter response histograms as features, and slightly better performance than using the full filter response histograms instead. Classification results are better than some of those previously reported in the literature.
Originalsprog | Engelsk |
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Titel | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 : 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I |
Redaktører | D. Metaxas, L. Axel, G. Fichtinger, G. Szekely |
Antal sider | 8 |
Forlag | Springer |
Publikationsdato | 2008 |
Sider | 934-941 |
ISBN (Trykt) | 9783540859871 |
DOI | |
Status | Udgivet - 2008 |
Begivenhed | International Conference on Medical Image Computing and Computer-Assisted Intervention - New York, N.Y., USA Varighed: 6 sep. 2008 → 10 sep. 2008 Konferencens nummer: 11 |
Konference
Konference | International Conference on Medical Image Computing and Computer-Assisted Intervention |
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Nummer | 11 |
Land | USA |
By | New York, N.Y. |
Periode | 06/09/2008 → 10/09/2008 |
Navn | Lecture notes in computer science |
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Nummer | 5241 |
ISSN | 0302-9743 |
ID: 6474573