微血管成像模块获取的结膜血管网络图像和动态血流视频,需进一步应用成像算法处理,以提取定量的血流形态学和血流动力学功能参数。图像处理软件的开发已在之前的文献中叙述[31-32]。对于结膜血管网络图像,首先,通过自适应直方图均衡化来提高图像质量,然后通过减去背景图像来进一步增强图像质量;然后,将增强后的图像转换为二值图像,利用骨架化算法得到网络骨架;最后,对血管骨架图像进行分析,计算分形维数复度,提取反应血管网复杂度的分形维数单分形值(Dbox)和反应血管网密度的多重分形(D0)值,用于评价血管密度和复杂性。对于结膜血流影像,首先采用基于球结膜时空图像的运动校正算法,对眼球运动进行补偿,得到配准图像。在对血管图像进行增强和分割后,利用多帧图像的均值,通过计算血管信号轮廓的半高宽(full width at half maxima,FWHM)来测量血管直径(D,μm)。通过分析红细胞的运动,定量结膜血流动力学参数,直接测出轴向血流速度(Va,mm/s),以此计算出截面血流速度,截面血流速度乘以血管横截面积(假设血管为圆形截面,由血管直径计算得出)获得血流量(Q,pl/s)[48]。
(A, B) Slit-lamp corneal imaging of healthy subjects, the red dashed line indicates the OCT scanning position. (C, D) OCT image of the healthy cornea and the zoom-in area showing clear corneal layer structures. EP: epithelial layer; BL: Bowman’s layer; ST: stroma; ED: endothelial layer. (E) Screenshots of eye microvascular dynamics in the selected area of conjunctival microvascular network. (F) The bulbar conjunctival vessel network image of the healthy subject. (G, H) The segmented vessel network image and the skeletonized vessel network image of the selected region.
图3 角膜炎患者多模态成像
Figure 3 Experimental results of the keratitis patient
(A) Appearance image of the keratitis patient’s eye; the red dashed line indicates the OCT scanning position; (B) Fluorescein stain image of the keratitis patient; (C, D) OCT image of the inflamed cornea and the zoom-in area showing the highly reflective, inflamed lesion.(E) Screenshots of eye microvascular dynamics in the selected area of conjunctival microvascular network; (F) The bulbar conjunctival vessel network image of the keratitis patient; (G, H) Results of skeletonization of conjunctival microvascular network.
表1 血流形态学及动力学参数对比
Table 1 Comparison of the quantitative vessel parameters
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