Texture Classification in Lung CT Using Local Binary Patterns

L Sorensen, SB Shaker, Marleen de Bruijne

Research output: Contribution to journalArticleAcademicpeer-review

38 Citations (Scopus)


In this paper we propose to rise local binary patterns (LBP) as features in a classification framework for classifying different texture patterns in lung computed tomography. linage 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.
Original languageUndefined/Unknown
Pages (from-to)934-941
Number of pages8
JournalLecture Notes in Computer Science
Publication statusPublished - 2008

Research programs

  • EMC NIHES-03-30-03

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