首页> 外文会议>IEEE International Symposium on Biomedical Imaging >A GMM-based feature extraction technique for the automated diagnosis of Retinopathy of Prematurity
【24h】

A GMM-based feature extraction technique for the automated diagnosis of Retinopathy of Prematurity

机译:基于GMM的特征提取技术,可自动诊断早产儿视网膜病变

获取原文

摘要

Retinopathy of Prematurity (ROP) is an ophthalmic disease that is a leading cause of childhood blindness throughout the world. Accurate diagnosis of ROP is vital to identify infants who require treatment, which can prevent blindness. Arterial tortuosity and venous dilation in the retina are important signs of ROP, so it is necessary to extract these features from points on the vessels or vessel segments. Then, an image is represented with statistics such as minimum, maximum or mean of these values. However, these statistics provide biased estimates as an image contains both healthy and abnormal vessels. In this work, we present a novel feature extraction technique that represents each image with the parameters of a two-component Gaussian Mixture Model (GMM). Using these features, we performed classification experiments on a manually segmented retinal image dataset consisting of 77 images. The results show that GMM-based features outperform other features that are based on classical statistics, with accuracy over 90%. Moreover, if the features are extracted from the whole image without distinguishing veins and arteries, proposed features provide better performance compared to using traditional statistics.
机译:早产儿视网膜病变(ROP)是一种眼科疾病,是全世界儿童失明的主要原因。 ROP的准确诊断对于识别需要治疗的婴儿至关重要,因为这可以预防失明。视网膜中的动脉曲折和静脉扩张是ROP的重要征象,因此有必要从血管或血管段上的点中提取这些特征。然后,用诸如这些值的最小值,最大值或平均值之类的统计量表示图像。但是,由于图像同时包含健康血管和异常血管,因此这些统计数据提供有偏差的估计。在这项工作中,我们提出了一种新颖的特征提取技术,该技术用两组分高斯混合模型(GMM)的参数表示每个图像。利用这些功能,我们对由77张图像组成的人工分割的视网膜图像数据集进行了分类实验。结果表明,基于GMM的特征优于基于经典统计信息的其他特征,其准确率超过90%。此外,如果从整个图像中提取特征而又不区分静脉和动脉,那么与使用传统统计数据相比,提出的特征将提供更好的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号