Tian Shu, Yin Xu-Cheng, Wang Zhi-Bin, Zhou Fang, Hao Hong-Wei
Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
National Engineering Research Center for Information Technology in Agriculture, Beijing 100089, China.
Comput Math Methods Med. 2015;2015:202934. doi: 10.1155/2015/202934. Epub 2015 Nov 26.
The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness.
超声乳化手术是治疗白内障最先进的手术之一。然而，传统手术的操作自动化程度始终较低，且过度依赖外科医生的能力。另一种设想是在手术过程中利用视频处理和模式识别技术自动检测白内障等级并智能控制超声能量的释放。与使用静态图像的诊断系统中的白内障分级不同，手术动态视频中总会引入复杂的背景、意外噪声和多样的信息。在此，我们开发了一种基于视频的智能识别与决策（VeBIRD）系统，该系统通过提供一个通用框架来自动跟踪手术过程并对超声乳化白内障手术显微镜视频中的白内障等级进行分类，从而开辟了新领域。VeBIRD包括一个采用随机霍夫变换的强大眼部（虹膜）检测器，用于在噪声背景中精确定位眼睛；一个采用跟踪-学习-检测的有效探头跟踪器，用于在动态过程中跟踪手术探头；以及一个采用判别学习的智能决策器，用于最终在复杂视频中识别白内障等级。对各种超声乳化手术的真实显微镜视频进行的实验验证了VeBIRD的有效性。
