Design considerations for a computer-vision-enabled ophthalmic augmented reality environment
Design considerations for a computer-vision-enabled ophthalmic augmented reality environment
复制标题
支持计算机视觉的眼科增强现实环境的设计注意事项
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
R. Kikinis
中科院分区：
文献类型：
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作者：
J. Berger;M. Leventon;N. Hata;W. Wells;R. Kikinis
我们已经开始了对眼科增强现实环境的设计和实施的研究，以允许a）更精确的激光治疗眼科疾病，B）教学，c）远程医疗，和d）实时图像测量，分析和比较。拟议的系统是围绕标准裂隙灯生物显微镜设计的。显微镜将连接到一个CCD相机，图像发送到视频捕获板。单个计算机工作站将协调图像捕获、配准和显示。所捕获的图像与先前存储的、剪辑的摄影和血管造影数据配准，并通过基于眼底标志的快速配准算法进行叠加。然后，计算机驱动连接到裂隙灯生物显微镜的一个目镜上的具有可调节亮度和对比度的高强度、VGA分辨率视频显示器。初步研究与修改后的双目手术显微镜连接到一个Sun的Ultral工作站和IBM兼容的PC演示的原理证明。鲁棒的，准确的眼底图像蒙太奇是用基于Hausdorff距离的方法来完成的。对于血管灰度从亮到暗变化的摄影和血管造影数据，并且基于强度的相关方法失败，具有平滑、边缘检测和阈值化的图像预处理有助于配准。非实时配准（0.4-4.0 CPU秒）是通过对边缘检测的眼底摄影和血管造影图像以及模型眼图像执行非优化的简单模板匹配（仅翻译，Matrox Inspector）或基于Hausdorff距离的算法（翻译、旋转和缩放）来实现的。证明了彩色、单色和血管造影图像的成功配准和图像叠加。据我们所知，这些研究代表了对眼科增强现实环境的设计和实现的第一次调查。
We have initiated studies towards the design and implementation of an ophthalmic augmented reality environment in order to allow for a) more precise laser treatment for ophthalmic diseases, b) teaching, c) telemedicine, and d) real-time image measurement, analysis, and comparison. The proposed system is being designed around a standard slit-lamp biomicroscope. The microscope will be interfaced to a CCD camera, and the image sent to a video capture board. A single computer workstation will coordinate image capture, registration, and display. The captured image is registered with previously stored, montaged photographic and angiographic data, with superposition facilitated by funduslandmark-based fast registration algorithms. The computer then drives a high intensity, VGA resolution video display with adjustable brightness and contrast attached to one of the oculars of the slitlamp biomicroscope. Preliminary studies with a modified binocular operating microscope interfaced to a Sun Ultral Workstation and an IBM-compatible PC demonstrates proof-of-principle. Robust, accurate fundus image montaging is accomplished with Hausdorff-distance-based methods. For photographic and angiographic data where the vessel gray levels vary from light to dark, and intensity-based correlation methods fail, image-preprocessing with smoothing, edge-detection, and thresholding facilitates registration. Non-real-time registration (∼ 0.4–4.0 CPU seconds) is achieved by non-optimized simple template matching (translation only, Matrox Inspector) or Hausdorff-distance-based (translation, rotation, and scale) algorithms performed on edge-detected fundus photographic and angiographic images, and on images of a model eye. Successful registration and image overlay of color, monochromatic, and angiographic images is demonstrated. To our knowledge, these studies represent the first investigation towards design and implementation of an ophthalmic augmented reality environment.