Texture Classification in Lung CT Using Local Binary Patterns

Lauge Emil Borch Laurs Sørensen; Saher B. Shaker; Marleen de Bruijne

doi:10.1007/978-3-540-85988-8_111

Texture Classification in Lung CT Using Local Binary Patterns

Lauge Emil Borch Laurs Sørensen, Saher B. Shaker, Marleen de Bruijne

Department of Computer Science

35 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 : 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I
Editors	D. Metaxas, L. Axel, G. Fichtinger, G. Szekely
Number of pages	8
Publisher	Springer
Publication date	2008
Pages	934-941
ISBN (Print)	9783540859871
DOIs	https://doi.org/10.1007/978-3-540-85988-8_111
Publication status	Published - 2008
Event	International Conference on Medical Image Computing and Computer-Assisted Intervention - New York, N.Y., United States Duration: 6 Sept 2008 → 10 Sept 2008 Conference number: 11

Conference

Conference	International Conference on Medical Image Computing and Computer-Assisted Intervention
Number	11
Country/Territory	United States
City	New York, N.Y.
Period	06/09/2008 → 10/09/2008

Series	Lecture notes in computer science
Number	5241
ISSN	0302-9743

Access to Document

10.1007/978-3-540-85988-8_111

Cite this

Sørensen, L. E. B. L., Shaker, S. B., & de Bruijne, M. (2008). Texture Classification in Lung CT Using Local Binary Patterns. In D. Metaxas, L. Axel, G. Fichtinger, & G. Szekely (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I (pp. 934-941). Springer. Lecture notes in computer science No. 5241 https://doi.org/10.1007/978-3-540-85988-8_111

Texture Classification in Lung CT Using Local Binary Patterns. / Sørensen, Lauge Emil Borch Laurs; Shaker, Saher B.; de Bruijne, Marleen.

Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I. ed. / D. Metaxas; L. Axel; G. Fichtinger; G. Szekely. Springer, 2008. p. 934-941 (Lecture notes in computer science; No. 5241).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Sørensen, LEBL, Shaker, SB & de Bruijne, M 2008, Texture Classification in Lung CT Using Local Binary Patterns. in D Metaxas, L Axel, G Fichtinger & G Szekely (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I. Springer, Lecture notes in computer science, no. 5241, pp. 934-941, International Conference on Medical Image Computing and Computer-Assisted Intervention, New York, N.Y., United States, 06/09/2008. https://doi.org/10.1007/978-3-540-85988-8_111

Sørensen LEBL, Shaker SB, de Bruijne M. Texture Classification in Lung CT Using Local Binary Patterns. In Metaxas D, Axel L, Fichtinger G, Szekely G, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I. Springer. 2008. p. 934-941. (Lecture notes in computer science; No. 5241). doi: 10.1007/978-3-540-85988-8_111

Sørensen, Lauge Emil Borch Laurs ; Shaker, Saher B. ; de Bruijne, Marleen. / Texture Classification in Lung CT Using Local Binary Patterns. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008: 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I. editor / D. Metaxas ; L. Axel ; G. Fichtinger ; G. Szekely. Springer, 2008. pp. 934-941 (Lecture notes in computer science; No. 5241).

@inproceedings{0678b610954111dd86a6000ea68e967b,

title = "Texture Classification in Lung CT Using Local Binary Patterns",

abstract = "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.",

author = "S{\o}rensen, {Lauge Emil Borch Laurs} and Shaker, {Saher B.} and {de Bruijne}, Marleen",

year = "2008",

doi = "10.1007/978-3-540-85988-8_111",

language = "English",

isbn = "9783540859871",

series = "Lecture notes in computer science",

publisher = "Springer",

number = "5241",

pages = "934--941",

editor = "D. Metaxas and L. Axel and G. Fichtinger and G. Szekely",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008",

note = "International Conference on Medical Image Computing and Computer-Assisted Intervention ; Conference date: 06-09-2008 Through 10-09-2008",

}

TY - GEN

T1 - Texture Classification in Lung CT Using Local Binary Patterns

AU - Sørensen, Lauge Emil Borch Laurs

AU - Shaker, Saher B.

AU - de Bruijne, Marleen

N1 - Conference code: 11

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-540-85988-8_111

DO - 10.1007/978-3-540-85988-8_111

M3 - Article in proceedings

SN - 9783540859871

T3 - Lecture notes in computer science

SP - 934

EP - 941

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008

A2 - Metaxas, D.

A2 - Axel, L.

A2 - Fichtinger, G.

A2 - Szekely, G.

PB - Springer

T2 - International Conference on Medical Image Computing and Computer-Assisted Intervention

Y2 - 6 September 2008 through 10 September 2008

ER -

Texture Classification in Lung CT Using Local Binary Patterns

Abstract

Conference

Access to Document

Fingerprint

Cite this