紧凑型白内障筛查系统:设计与实用数据采集

R. Supriyanti, H. Habe, M. Kidode, S. Nagata
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引用次数: 19

摘要

白内障患者数量的增加是一个严重的问题,因为白内障是世界上致盲的主要原因。为了避免白内障致盲,我们需要及早发现。今天,眼科医生使用裂隙灯来诊断白内障。这种设备很贵,需要经过特殊培训才能使用。不幸的是,许多发展中国家的眼科医生和医疗设施数量有限,而许多白内障患者生活在发展中国家。我们的算法是针对这样的发展中国家和农村地区,其主要设备是一个小型数码相机。这种设备价格低廉,任何人都可以轻松使用,无需经过专门培训。检查可以很容易地在门诊进行。我们已经提出了一种考虑瞳孔内部镜面反射和统计纹理外观的设备的白内障筛查方法。然而,为了实现我们的目标,不仅仅是筛选方法本身,还有其他方面,如紧凑性和易用性是至关重要的。本文将讨论一种紧凑型白内障筛查系统的系统设计。我们研究的最终目标是开发一种简单而强大的白内障筛查系统与一个紧凑的数码相机。用户只需拍下病人的照片,系统就会通过提取瞳孔内部的信息来自动分析图像,包括镜面反射分析和纹理外观分析,以区分严重和非严重的疾病。我们强调了如何在实际条件下实现我们的系统,并给出了在拍照时获取适当图像数据的建议,使我们的系统能够正常工作。实际实现的结果与我们的建议类似。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compact cataract screening system : Design and practical data acquisition
The increasing number of cataract sufferers is a serious problem because cataracts are a leading cause of blindness in the world. To avoid blindness from cataracts, we need to detect them early. Today, ophthalmologists use a slit lamp to diagnose cataracts. This equipment is expensive and requires special training to use it. Unfortunately, a lot of developing countries have a limited number of ophthalmologists and health facilities, while a lot of cataract sufferers live in developing countries. Our algorithm is aimed at such developing countries and rural areas, and its main equipment is a compact digital camera. This equipment is inexpensive and easy for anyone to use without special training. Examination can be easily carried out in an outpatient department. We have already proposed a cataract screening method for the equipment that considers about specular reflection and statistical texture appearance inside the pupil. However, in order to accomplish our goal, not only the screening method itself, but also other aspects such as compactness and ease of use are crucial. In this paper, we will discuss system design for a compact cataract screening system. The final goal of our research is developing a simple and robust screening system for cataract with a compact digital camera. A user simply takes a patient's photograph and the system will automatically analyze the image by extracting information inside a pupil, including specular reflection analysis and texture appearance analysis, for distinguishing between serious and non-serious conditions. We emphasize how to implement our system in real conditions by giving a recommendation for acquiring appropriate image data while taking a photograph so that our system will work well. The results in a real implementation are similar to our recommendation.
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