Hierarchical local binary pattern for branch retinal vein occlusion recognition

Zenghai Chen, Hui Zhang, Zheru Chi, Hong Fu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Branch retinal vein occlusion (BRVO) is one of the most common retinal vascular diseases of the elderly that would dramatically impair one’s vision if it is not diagnosed and treated timely. Automatic recognition of BRVO could significantly reduce an ophthalmologist’s workload, make the diagnosis more efficient, and save the patients’ time and costs. In this paper, we propose for the first time, to the best of our knowledge, automatic recognition of BRVO using fundus images. In particular, we propose Hierarchical Local Binary Pattern (HLBP) to represent the visual content of an fundus image for classification. HLBP is comprised of Local Binary Pattern (LBP) in a hierarchical fashion with max-pooling. In order to evaluate the performance of HLBP, we establish a BRVO dataset for experiments. HLBP is compared with several state-of-the-art feature presentation methods on the BRVO dataset. Experimental results demonstrate the superior performance of our proposed method for BRVO recognition.
Original languageEnglish
Title of host publicationComputer Vision - ACCV 2014 Workshops - Revised Selected Papers
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319166278
Publication statusPublished - 1 Jan 2015
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014
Conference number: 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Asian Conference on Computer Vision, ACCV 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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