Information and Human Science, Kyoto Institute of Technology University, Kyoto 6068585, Japan.
Retina & Neuron-Ophthalmology, Tianjin Medical University Eye Hospital, Tianjin 300084, China.
Sensors (Basel). 2022 Sep 13;22(18):6899. doi: 10.3390/s22186899.
The accurate segmentation of the optic disc (OD) in fundus images is a crucial step for the analysis of many retinal diseases. However, because of problems such as vascular occlusion, parapapillary atrophy (PPA), and low contrast, accurate OD segmentation is still a challenging task. Therefore, this paper proposes a multiple preprocessing hybrid level set model (HLSM) based on area and shape for OD segmentation. The area-based term represents the difference of average pixel values between the inside and outside of a contour, while the shape-based term measures the distance between a prior shape model and the contour. The average intersection over union (IoU) of the proposed method was 0.9275, and the average four-side evaluation (FSE) was 4.6426 on a public dataset with narrow-angle fundus images. The IoU was 0.8179 and the average FSE was 3.5946 on a wide-angle fundus image dataset compiled from a hospital. The results indicate that the proposed multiple preprocessing HLSM is effective in OD segmentation.
眼底图像中视盘（OD）的精确定位是分析许多视网膜疾病的关键步骤。然而，由于血管阻塞、视盘旁萎缩（PPA）和对比度低等问题，准确的 OD 分割仍然是一项具有挑战性的任务。因此，本文提出了一种基于面积和形状的多预处理混合水平集模型（HLSM）用于 OD 分割。基于面积的项表示轮廓内外平均像素值的差异，而基于形状的项则测量先验形状模型与轮廓之间的距离。在一个带有窄角眼底图像的公共数据集上，所提出方法的平均交并比（IoU）为 0.9275，平均四边评价（FSE）为 4.6426。在一个由医院编译的广角眼底图像数据集上，IoU 为 0.8179，平均 FSE 为 3.5946。结果表明，所提出的多预处理 HLSM 对视盘分割是有效的。
