Texture Classification in Lung CT Using Local Binary Patterns

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

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.
TitelMedical Image Computing and Computer-Assisted Intervention - MICCAI 2008 : 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I
RedaktørerD. Metaxas, L. Axel, G. Fichtinger, G. Szekely
Antal sider8
ISBN (Trykt)9783540859871
StatusUdgivet - 2008
BegivenhedInternational Conference on Medical Image Computing and Computer-Assisted Intervention - New York, N.Y., USA
Varighed: 6 sep. 200810 sep. 2008
Konferencens nummer: 11


KonferenceInternational Conference on Medical Image Computing and Computer-Assisted Intervention
ByNew York, N.Y.
NavnLecture notes in computer science

ID: 6474573