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 proceedingConference contribution

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 languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-96
Number of pages4
ISBN (Electronic)9781467319591
DOIs
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'OCM image texture analysis for tissue classification'. Together they form a unique fingerprint.

Cite this