Automatic Detection of Hard Exudates in Retinal Images Using Haar Wavelet Transform
Automatic Detection of Hard Exudates in Retinal Images Using Haar Wavelet Transform
复制标题
使用 Haar 小波变换自动检测视网膜图像中的硬渗出物
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
R. Manza
中科院分区：
文献类型：
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作者：
Poonam M. Rokade;R. Manza
糖尿病视网膜病变（DR）是导致视力丧失的主要原因，由体内胰岛素不足导致视网膜异常引起。因此，糖尿病患者需要定期体检，以便及时有效地进行视力保护治疗。一个完全自动化的、强大的糖尿病视网膜病变筛查系统可以有效地减轻专家的负担，节省成本和时间。由于在图像采集过程中出现的噪声和其他干扰，DR可能导致错误检测，这可以通过各种图像处理技术来克服。本文提出了一种自动检测视网膜图像中明亮病变即硬渗出物的方法。系统采用Haar小波变换对硬渗出液进行分割，然后采用k近邻分类方法进行分割。我们使用了四个眼底图像数据库；其中MISP、DB0、DB1和STARE数据库的灵敏度分别为37.14%、21.87%、12.50%、25.47%。MISP的特异性为0%，其余数据库的特异性为1%。
Diabetic Retinopathy (DR) is a leading cause of vision loss, caused by the abnormalities in the retina due to insufficient insulin in the body. So that Diabetic patients require regular medical checkup for effective timing of sight saving treatment. A completely automated and robust screening system for the detection of Diabetic Retinopathy can effectively reduces the burden of the specialist and saves cost as well as time. Due to noise and other disturbances that occur during image acquisition, DR may lead to false detection and this is overcome by various image processing techniques. This paper presents an automated method for bright lesions i.e. hard exudates in retinal images. The Haar wavelets transform is used in the system for the hard exudates segmentation followed by k nearest neighbor classification method. We have used four databases of fundus images; among them we obtain sensitivity 37.14%, 21.87%, 12.50%, 25.47% for MISP, DB0, DB1 and STARE database respectively. And the specificity is 0% for MISP and 1% for remained databases.