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Wavelet-Based Computer-Aided Detection of Bright Lesions in Retinal Fundus Images

机译:基于小波的视网膜眼底图像明亮病变计算机辅助检测

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Computer-aided detection and diagnosis of diabetic retinopa-thy with retinal fundus images is the necessary step for the implementation of a large scale screening effort in regions where ophthalmologists are not available. In this paper we propose computer-aided binary detector of bright lesions in retinal fundus images. It is based on wavelets for mul-tiresolution feature discrimination and support vector machine (SVM) for classification. After thresholding the sub-band images resulting from the Isotropic Undecimated Wavelet Transform (IUWT) decomposition of the input image, we employ an approach based on the image Hessian eigenvalues and multi-scale image analysis, for designing good feature descriptors of bright lesions. These are afterwards used in the SVM model classifier. Experimental results on our current data set show that the proposed method is efficient and achieves a very good success rate.
机译:在没有眼科医生的地区,使用视网膜眼底图像对糖尿病性视网膜病进行计算机辅助检测和诊断是实施大规模筛查工作的必要步骤。在本文中,我们提出了一种计算机辅助的视网膜底图像明亮病变的二进制检测器。它基于小波进行多分辨率特征识别,并基于支持向量机(SVM)进行分类。在对输入图像的各向同性未抽取小波变换(IUWT)分解产生的子带图像进行阈值处理之后,我们采用基于图像Hessian特征值和多尺度图像分析的方法来设计明亮病变的良好特征描述符。这些之后将在SVM模型分类器中使用。在我们当前数据集上的实验结果表明,该方法是有效的,并且取得了很好的成功率。

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