Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore 138632.
IEEE Trans Biomed Eng. 2010 Oct;57(10):2605-8. doi: 10.1109/TBME.2010.2055057. Epub 2010 Jun 28.
Under the framework of computer-aided diagnosis, optical coherence tomography (OCT) has become an established ocular imaging technique that can be used in glaucoma diagnosis by measuring the retinal nerve fiber layer thickness. This letter presents an automated retinal layer segmentation technique for OCT images. In the proposed technique, an OCT image is first cut into multiple vessel and nonvessel sections by the retinal blood vessels that are detected through an iterative polynomial smoothing procedure. The nonvessel sections are then filtered by a bilateral filter and a median filter that suppress the local image noise but keep the global image variation across the retinal layer boundary. Finally, the layer boundaries of the filtered nonvessel sections are detected, which are further classified to different retinal layers to determine the complete retinal layer boundaries. Experiments over OCT for four subjects show that the proposed technique segments an OCT image into five layers accurately.
在计算机辅助诊断的框架下，光学相干断层扫描（OCT）已成为一种成熟的眼部成像技术，可通过测量视网膜神经纤维层厚度用于青光眼的诊断。本文提出了一种用于 OCT 图像的自动视网膜分层分割技术。在该技术中，首先通过迭代多项式平滑过程检测视网膜血管，将 OCT 图像分割成多个血管和非血管部分。然后，通过双边滤波器和中值滤波器对非血管部分进行滤波，该滤波器可以抑制局部图像噪声，同时保持视网膜层边界的全局图像变化。最后，检测滤波后的非血管部分的层边界，并将其进一步分类到不同的视网膜层，以确定完整的视网膜层边界。对四名受试者的 OCT 实验表明，该技术可以准确地将 OCT 图像分割成五层。
