基于CBIR技术的眼底图像自动分类检索系统

Content-Based Automatic Retinal Image Recognition and Retrieval System

为实现一个眼底图像自动识别检索的原型系统,本文采用基于内容的图像检索(CBIR)技术提出一种综合考虑眼底图像颜色(灰度)直方图和明、暗区域等局部信息相混合来表示眼底特征的方法,运用核主成分分析(KP-CA)法进一步提取非线性特征和降维。在相似性度量上,提出一种利用支持向量机(SVM)对KPCA加权距离来度量的方法。用该系统随机测试300个样本,检索错误的图片总数为32张,其检索率为89.33%。实验表明该原型系统对眼底图像的识别率极高。

This paper is aimed to fulfill a prototype system used to classify and retrieve retinal image automatically. With the content-based image retrieval （CBIR） technology, a method to represent the retinal characteristics mixing the fundus image color （gray） histogram with bright, dark region features and other local comprehensive information was proposed. The method uses kernel principal component analysis （KPCA） to further extract nonlinear features and dimensionality reduced. It also puts forward a measurement method using support vector machine （SVM） on KPCA weighted distance in similarity measure aspect. Testing 300 samples with this prototype system randomly, we obtained the total image number of wrong retrieved 32, and the retrieval rate 89.33%. It showed that the identifica- tion rate of the system for retinal image was high.