OCM image texture analysis for tissue classification

Sunhua Wan; Hsiang Chieh Lee; James G. Fujimoto; Xiaolei Huang; Chao Zhou

doi:10.1109/isbi.2014.6867817

OCM image texture analysis for tissue classification

Sunhua Wan, Hsiang Chieh Lee, James G. Fujimoto, Xiaolei Huang, Chao Zhou

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

3 Scopus citations

Abstract

This paper proposes a texture analysis technique applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Local binary pattern (LBP) image features are extracted. In order to improve classification precision, a new variant of LBP feature, average LBP (ALBP) is proposed. The new LBP is integrated with the original LBP feature to improve classification precision. Our experiments show that by integrating a selected set of LBP and ALBP features, very high classification accuracy is achieved using a AdaBoost meta classifier combined with neural network weak classifiers.

Original language	English
Title of host publication	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	93-96
Number of pages	4
ISBN (Electronic)	9781467319591
DOIs	https://doi.org/10.1109/isbi.2014.6867817
State	Published - Jul 29 2014
Event	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China Duration: Apr 29 2014 → May 2 2014

Publication series

Name	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Conference

Conference	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Country/Territory	China
City	Beijing
Period	04/29/14 → 05/2/14

Keywords

Image analysis
Local binary pattern
Optical coherence microscopy (OCM)
Texture analysis
Tissue classification

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10.1109/isbi.2014.6867817

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Wan, S., Lee, H. C., Fujimoto, J. G., Huang, X., & Zhou, C. (2014). OCM image texture analysis for tissue classification. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 93-96). Article 6867817 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6867817

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title = "OCM image texture analysis for tissue classification",

abstract = "This paper proposes a texture analysis technique applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Local binary pattern (LBP) image features are extracted. In order to improve classification precision, a new variant of LBP feature, average LBP (ALBP) is proposed. The new LBP is integrated with the original LBP feature to improve classification precision. Our experiments show that by integrating a selected set of LBP and ALBP features, very high classification accuracy is achieved using a AdaBoost meta classifier combined with neural network weak classifiers.",

keywords = "Image analysis, Local binary pattern, Optical coherence microscopy (OCM), Texture analysis, Tissue classification",

author = "Sunhua Wan and Lee, {Hsiang Chieh} and Fujimoto, {James G.} and Xiaolei Huang and Chao Zhou",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",

year = "2014",

month = jul,

day = "29",

doi = "10.1109/isbi.2014.6867817",

language = "English",

series = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "93--96",

booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",

}

Wan, S, Lee, HC, Fujimoto, JG, Huang, X & Zhou, C 2014, OCM image texture analysis for tissue classification. in 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6867817, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 93-96, 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 04/29/14. https://doi.org/10.1109/isbi.2014.6867817

OCM image texture analysis for tissue classification. / Wan, Sunhua; Lee, Hsiang Chieh; Fujimoto, James G. et al.
2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 93-96 6867817 (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014).

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

TY - GEN

T1 - OCM image texture analysis for tissue classification

AU - Wan, Sunhua

AU - Lee, Hsiang Chieh

AU - Fujimoto, James G.

AU - Huang, Xiaolei

AU - Zhou, Chao

PY - 2014/7/29

Y1 - 2014/7/29

N2 - This paper proposes a texture analysis technique applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Local binary pattern (LBP) image features are extracted. In order to improve classification precision, a new variant of LBP feature, average LBP (ALBP) is proposed. The new LBP is integrated with the original LBP feature to improve classification precision. Our experiments show that by integrating a selected set of LBP and ALBP features, very high classification accuracy is achieved using a AdaBoost meta classifier combined with neural network weak classifiers.

AB - This paper proposes a texture analysis technique applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Local binary pattern (LBP) image features are extracted. In order to improve classification precision, a new variant of LBP feature, average LBP (ALBP) is proposed. The new LBP is integrated with the original LBP feature to improve classification precision. Our experiments show that by integrating a selected set of LBP and ALBP features, very high classification accuracy is achieved using a AdaBoost meta classifier combined with neural network weak classifiers.

KW - Image analysis

KW - Local binary pattern

KW - Optical coherence microscopy (OCM)

KW - Texture analysis

KW - Tissue classification

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U2 - 10.1109/isbi.2014.6867817

DO - 10.1109/isbi.2014.6867817

M3 - Conference contribution

AN - SCOPUS:84927924404

T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

SP - 93

EP - 96

BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Y2 - 29 April 2014 through 2 May 2014

ER -

OCM image texture analysis for tissue classification

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