Real-Time Processing and Multi-Spectral Imaging Equipment for Intraocular Image Processing

用于眼内图像处理的实时处理和多光谱成像设备

• 批准号: 9634206
• 负责人: Badrinath Roysam
• 金额: $ 2.15万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-09-01 至 1997-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9634206&HistoricalAwards=false
• 关键词: Real; Time; Processing; Multi; Spectral

This research involves two components. The first component is an extension of prior work under NSF grant MIP-9412500 on the development of algorithms for automatic generation of retinal vasculature maps, and real-time feature-based determination of retinal position from intra-ocular digital video image sequences. Specifically, a TMS320C80 video signal processor chip will be used to achieve fully transparent image tracking by reducing the time required to establish a tracking lock to a small number of video frame intervals. The second component to this research is to modify the fundus imaging system acquired under the previous award to achieve simultaneous imaging in the near infrared and visible bands using a pair of low-light video cameras. This instrumentation will be used to investigate real-time algorithms for artificially superimposing computed vasculature landmarks on late-phase Indocyanine Green angiograms. This research will result in the development of real-time image processing hardware and software technology for enabling the development of novel instrumentation for laser retinal surgery and related applications. It is motivated by the need to reduce the currently high failure rate (approaching 50%) of current procedures. This project is an application of high-performance computing technology to enable new advances in ophthalmology and related problems in biology and medicine. In addition to reducing the failure rate, this technology is expected to reduce the cost of laser retinal surgery by eliminating the need for repeated corrective procedures.

这项研究包括两个部分。第一个组成部分是美国国家科学基金会授予MIP-9412500的先前工作的扩展，该工作旨在开发用于自动生成视网膜血管图的算法，以及根据眼内数字视频图像序列实时确定视网膜位置的基于特征的算法。具体地说，将使用TMS320C80视频信号处理器芯片，通过将建立跟踪锁所需的时间减少到少量视频帧间隔来实现完全透明的图像跟踪。这项研究的第二个组成部分是修改上一次获奖时获得的眼底成像系统，以使用一对微光摄像机在近红外和可见光波段实现同时成像。该仪器将用于研究在晚期吲哚青绿血管造影术中人工叠加计算血管标志物的实时算法。这项研究将导致实时图像处理硬件和软件技术的发展，从而使激光视网膜手术和相关应用的新型仪器的开发成为可能。其动机是需要降低当前程序的高失败率(接近50%)。该项目是高性能计算技术的应用，以实现眼科以及生物学和医学中相关问题的新进展。除了降低失败率，这项技术还有望通过消除重复的矫正程序来降低激光视网膜手术的成本。