Automatic Retinal Layer Segmentation Based on Live Wire for Central Serous Retinopathy
Central serous retinopathy is a serious retinal disease. Retinal layer segmentation for this disease can help ophthalmologists to provide accurate diagnosis and proper treatment for patients. In order to detect surfaces in optical coherence tomography images with pathological changes, an automatic method is reported by combining random forests and a live wire algorithm. First, twenty four features are designed for the random forest classifiers to find initial surfaces. Then, a live wire algorithm is proposed to accurately detect surfaces between retinal layers even though OCT images with fluids are of low contrast and layer boundaries are blurred. The proposed method was evaluated on 24 spectral domain OCT images with central serous retinopathy. The experimental results showed that the proposed method outperformed the state-of-art methods.
KeywordsCentral serous retinopathy Optical coherence tomography Random forest and live wire
This work has been supported in part by the National Basic Research Program of China (973 Program) under Grant 2014CB748600, and in part by the National Natural Science Foundation of China (NSFC) under Grant 81371629, 61401293, 61401294, 81401451, 81401472.
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