《A computer-aided healthcare system for cataract classification and grading based on fundus image analysis》学习笔记

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Abstract

       This paper presents a fundus image analysis based computer aided system for automatic classification and grading of cataract, which provides great potentials to reduce the burden of well-experienced ophthalmologists (the scarce resources) and help cataract patients in under-developed areas to know timely their cataract conditions and obtain treatment suggestions from doctors. The system is composed of fundus image pre-processing, image feature extraction, and automatic cataract classification and grading. The wavelet transform and the sketch based methods are investigated to extract from fundus image the features suitable for cataract classification and grading. After feature extraction, a multiclass discriminant analysis algorithm is used for cataract classification, including two-class (cataract or non-cataract) classification and cataract grading in mild, moderate, and severe. A real-world dataset, including fundus image samples with mild, moderate, and severe cataract, is used for training and testing. The preliminary results show that, for the wavelet transform based method, the correct classification rates of two-class classification and cataract grading are 90.9% and 77.1%, respectively. The correct classification rates of two-class classification and cataract grading are 86.1% and 74.0% for the sketch based method, which is comparable to the wavelet transform based method. The pilot study demonstrates that our research on fundus image analysis for cataract classification and grading is very helpful for improving the efficiency of fundus image review and ophthalmic healthcare quality. We believe that this work can serve as an important reference for the development of similar health information system to solve other medical diagnosis problems.

    本文提出了一种自动分类和白内障的分级，它提供了巨大的潜力，以减少经验丰富的眼科医生的负担（稀缺资源），帮助白内障患者在欠发达地区要及时了解眼底图像分析基于计算机辅助系统，其白内障条件，从医生获得处理意见。该系统是由眼底图像预处理，图像特征提取和自动白内障分类和分级。小波变换和基于草图方法进行了研究，从眼底图像中提取适用于白内障分类分级的功能。特征提取后，多类判别分析算法用于白内障的分类，包括在轻度，中度和重度两舱（白内障或无白内障）的分类和分级白内障。一个真实世界的数据集，包括轻度，中度，重度白内障眼底图像样本，用于训练和测试。初步结果表明，对于小波变换为基础的方法的二类别分类和白内障分级的正确分类率分别为90.9％和77.1％。的二分类和分级白内障的正确分类率是86.1％，而基于草图方法，这是堪比基于小波变换的方法，74.0％。试点研究表明，我们对白内障分类分级眼底图像分析的研究是改善眼底图像审查和眼科医疗服务质量的效率非常有帮助。我们相信，这项工作可以作为类似的卫生信息系统的发展，以解决其他医疗诊断问题的重要参考。

《A computer-aided healthcare system for cataract classification and grading based on fundus image analysis》学习笔记

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原文地址：http://www.cnblogs.com/leileiyiyi/p/5801982.html