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. Copyright © 2015 Springer International Publishing Switzerland.
|Title of host publication||Computer vision - ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, revised selected papers, part I|
|Editors||C.V. JAWAHAR, Shiguang SHAN|
|Place of Publication||Cham|
|Publication status||Published - 2015|
CitationChen, Z., Zhang, H., Chi, Z., & Fu, H. (2015). Hierarchical local binary pattern for branch retinal vein occlusion recognition. In C. V. Jawahar & S. Shan (Eds.), Computer vision - ACCV 2014 Workshops: Singapore, Singapore, November 1-2, 2014, revised selected papers, part I (pp. 687-697). Cham: Springer.
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- Local Binary Pattern
- Central pixel
- Convolutional neural network
- Linear kernel