Spoke-LBP and ring-LBP: New texture features for tissue classification

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations


This paper proposes a texture feature which is applied on human breast Optical Coherence Microscopy (OCM) images to classify different types of breast tissues. Inspired by local binary pattern (LBP) texture features, a new variant of LBP feature, block based LBP (BLBP) is proposed. Instead of representing intensity differences between neighbors and a center pixel, BLBP feature extracts the intensity differences among certain blocks of the neighborhood around a pixel. Two different ways are proposed to organize the blocks: the spokes and the rings. By integrating spoke BLBP with ring BLBP features, very high classification accuracy is achieved using a neural network classifier. In one of our experiments which classifies 4310 OCM images into five tissue types, the classification accuracy increased from 81.7% to 92.4% when new features are used instead of the traditional LBP feature. In another experiment which classifies 46 large field OCM images as either benign or containing tumor, a classification accuracy of 91.3% is reached by using multi-scale BLBP features.

Original languageEnglish
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479923748
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452


Conference12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/TerritoryUnited States


  • Local Binary Pattern (LBP)
  • Optical Coherence Microscopy (OCM)
  • Texture feature
  • Tissue classification
  • Tumor detection


Dive into the research topics of 'Spoke-LBP and ring-LBP: New texture features for tissue classification'. Together they form a unique fingerprint.

Cite this