从裂隙灯眼底生物显微镜视频图像序列中自动实时提取眼底图像。
Automated, real time extraction of fundus images from slit lamp fundus biomicroscope video image sequences.
作者信息
Madjarov B D, Berger J W
机构信息
Computer Vision Laboratory, Scheie Eye Institute, University of Pennsylvania, Philadelphia 19104, USA.
出版信息
Br J Ophthalmol. 2000 Jun;84(6):645-7. doi: 10.1136/bjo.84.6.645.
AIMS
Slit lamp fundus biomicroscopy allows for high magnification, stereoscopic diagnosis, and treatment of macular diseases. Variable contrast, narrow field of view, and specular reflections arising from the cornea, sclera, and examining lens reduce image quality; these images are of limited clinical utility for diagnosis, treatment planning, and photodocumentation when compared with fundus camera images. Algorithms are being developed to segment fundus imagery from slit lamp biomicroscopic video image sequences in order to improve clinical utility.
METHODS
Video fundus image sequences of human volunteers were acquired with a video equipped, Nikon NS-1V slit lamp biomicroscope. Custom developed software identified specular reflections based on brightness and colour content, and extracted the illuminated fundus image based on colour image analysis and size constraints.
RESULTS
In five subjects with variable image quality, the approach allowed for automatic, robust, accurate extraction of that portion of the video image corresponding to the illuminated portion of the fundus. Non-real time analysis allowed for fundus image segmentation for each frame of the image sequence. In real time, segmentation occurs at 2 Hz, and improvements are being implemented for video rate performance.
CONCLUSIONS
Computer vision algorithms allow for real time extraction of fundus imagery from marginal quality, slit lamp fundus biomicroscope image sequences.
目的
裂隙灯眼底生物显微镜检查可对黄斑疾病进行高倍、立体诊断及治疗。然而，对比度变化、视野狭窄以及来自角膜、巩膜和检查透镜的镜面反射会降低图像质量；与眼底相机图像相比，这些图像在诊断、治疗规划和照片记录方面的临床应用价值有限。目前正在开发算法，以从裂隙灯生物显微镜视频图像序列中分割出眼底图像，从而提高其临床应用价值。
方法
使用配备视频功能的尼康NS-1V裂隙灯生物显微镜采集人类志愿者的视频眼底图像序列。定制开发的软件根据亮度和颜色内容识别镜面反射，并基于彩色图像分析和尺寸约束提取照明的眼底图像。
结果
在五名图像质量各异的受试者中，该方法能够自动、稳健且准确地提取视频图像中与眼底照明部分相对应的部分。非实时分析可对图像序列的每一帧进行眼底图像分割。实时情况下，分割频率为2赫兹，并且正在对视频速率性能进行改进。
结论
计算机视觉算法能够从质量欠佳的裂隙灯眼底生物显微镜图像序列中实时提取眼底图像。
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