Ţălu Ştefan, Stach Sebastian, Călugăru Dan Mihai, Lupaşcu Carmen Alina, Nicoară Simona Delia
Discipline of Descriptive Geometry and Engineering Graphics, Department of AET, Faculty of Mechanical Engineering, Technical University of Cluj-Napoca, 103-105 B-dul Muncii St., Cluj-Napoca 400641, Cluj, Romania.
Department of Biomedical Computer Systems, Institute of Informatics, Faculty of Computer Science and Materials Science, University of Silesia, Będzińska 39, 41-205 Sosnowiec, Poland.
Int J Ophthalmol. 2017 Mar 18;10(3):434-438. doi: 10.18240/ijo.2017.03.17. eCollection 2017.
To apply the multifractal analysis method as a quantitative approach to a comprehensive description of the microvascular network architecture of the normal human retina.
Fifty volunteers were enrolled in this study in the Ophthalmological Clinic of Cluj-Napoca, Romania, between January 2012 and January 2014. A set of 100 segmented and skeletonised human retinal images, corresponding to normal states of the retina were studied. An automatic unsupervised method for retinal vessel segmentation was applied before multifractal analysis. The multifractal analysis of digital retinal images was made with computer algorithms, applying the standard box-counting method. Statistical analyses were performed using the GraphPad InStat software.
The architecture of normal human retinal microvascular network was able to be described using the multifractal geometry. The average of generalized dimensions ( ) for =0, 1, 2, the width of the multifractal spectrum ( - ) and the spectrum arms' heights difference () of the normal images were expressed as mean±standard deviation (SD): for segmented versions, =1.7014±0.0057; =1.6507±0.0058; =1.5772±0.0059; =0.92441±0.0085; = 0.1453±0.0051; for skeletonised versions, =1.6303±0.0051; =1.6012±0.0059; =1.5531±0.0058; =0.65032±0.0162; = 0.0238±0.0161. The average of generalized dimensions ( ) for =0, 1, 2, the width of the multifractal spectrum () and the spectrum arms' heights difference () of the segmented versions was slightly greater than the skeletonised versions.
The multifractal analysis of fundus photographs may be used as a quantitative parameter for the evaluation of the complex three-dimensional structure of the retinal microvasculature as a potential marker for early detection of topological changes associated with retinal diseases.
应用多重分形分析方法作为一种定量手段,全面描述正常人视网膜微血管网络结构。
2012年1月至2014年1月期间,罗马尼亚克卢日-纳波卡眼科诊所招募了50名志愿者参与本研究。研究了一组100张对应视网膜正常状态的分割和骨架化的人类视网膜图像。在进行多重分形分析之前,应用了一种自动无监督的视网膜血管分割方法。使用计算机算法,应用标准盒计数法对数字视网膜图像进行多重分形分析。使用GraphPad InStat软件进行统计分析。
正常人视网膜微血管网络结构能够用多重分形几何来描述。正常图像在q = 0、1、2时的广义维数(Dq)平均值、多重分形谱宽度(Δf)和谱臂高度差(Δα)表示为均值±标准差(SD):对于分割版本,D0 = 1.7014±0.0057;D1 = 1.6507±0.0058;D2 = 1.5772±0.0059;Δf = 0.92441±0.0085;Δα = 0.1453±0.0051;对于骨架化版本,D0 = 1.6303±0.0051;D1 = 1.6012±0.0059;D2 = 1.5531±0.0058;Δf = 0.65032±0.0162;Δα = 0.0238±0.0161。分割版本在q = 0、1、2时的广义维数(Dq)平均值、多重分形谱宽度(Δf)和谱臂高度差(Δα)略大于骨架化版本。
眼底照片的多重分形分析可作为评估视网膜微血管复杂三维结构的定量参数,作为早期检测与视网膜疾病相关拓扑变化的潜在标志物。