Retinal Nerve Fiber Layer Segmentation of Spectral Domain Optical Coherence Tomography Images Based on Random Forest
CHEN Qiang①② XU Jun① NIU Sijie①③
①(School of Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China) ②(Fujan Provincial Key Laboratory of Iuformation Processing and Intelligent Control (Minjiang University), Fuzhou 350121, China) ③(School of Information Science and Engineering, University of Jinan, Jinan 250022, China)
Spectral Domain Optical Coherence Tomography (SD-OCT) imaging technique is widely used in the diagnosis of ophthalmology diseases. The segmentation of retinal layers plays a very important role in the diagnosis of glaucoma. In this paper, a random forest classifier is used which is trained by twelve different features to find the boundaries between layers. What’s more, the relative gray feature and the neighbor features are used to solve the problem of large errors under the condition of uneven illumination. In the last, the segmentation results of the proposed algorithm, a traditional algorithm and Iowa segmentation software on ten sets of retinal images are compared with manual segmentation, and the average absolute boundary errors are 9.20±2.57 μm, 11.33±2.99 μm, 10.27±3.01 μm, respectively. The experiments show that the proposed algorithm can segment the Retinal Never Fiber Layer (RNFL) better.
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