Open AccessHierarchical local binary pattern for branch retinal vein occlusion recognition with fluorescein angiography images
Author(s) -
Zhang Hui,
Chen Zenghai,
Chi Zheru,
Fu Hong
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2014.2854
Subject(s) - artificial intelligence , computer science , branch retinal vein occlusion , pattern recognition (psychology) , feature (linguistics) , computer vision , fluorescein angiography , feature extraction , representation (politics) , binary image , image processing , image (mathematics) , retinal , medicine , ophthalmology , macular edema , linguistics , philosophy , politics , political science , law
Branch retinal vein occlusion (BRVO) is one of the most common retinal diseases. Without timely diagnosis and treatment, it would seriously impair the patient's vision. Automatic recognition of BRVO could significantly improve the efficiency of diagnosis. A feature representation method is proposed for the automatic recognition of BRVO with fluorescein angiography (FA) images. The proposed feature representation method, termed hierarchical local binary pattern (HLBP), is comprised of LBPs in a hierarchical fashion with max‐pooling. A FA image dataset is established to evaluate the performance of the HLBP method. Experimental results demonstrate the superior performance of the proposed HLBP method for BRVO recognition with FA images, by comparing it with state‐of‐the‐art methods.