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首页> 外文会议>International Conference on Intelligent Systems and Control >Wavelet feature based SVM and NAIVE BAYES classification of glaucomatous images using PCA and Gabor filter
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Wavelet feature based SVM and NAIVE BAYES classification of glaucomatous images using PCA and Gabor filter

机译：基于小波特征的SVM和朴素贝叶斯使用PCA和Gabor滤波器的葡萄糖图像分类

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The increase in intraocular pressure within the eye causes degradation of optic nerves which results in glaucoma. It is an eye disease in which no early symptoms will be detected until some vision loss has occurred. Therefore diagnosing of glaucoma is very essential to minimize the risk of vision loss. In this paper, the input retinal images are enhanced by using Principal Component Analysis and the blood vessels are removes by Gabor filter, morphological operation and thresholding techniques. Glaucomatous image classification is performed using texture features of an image. The texture features are obtained using 2-D discrete wavelet transform (DWT). The filters used in this paper are symlet3 (sym3) and bi-orthogonal (bio3.3, bio3.5). The extracted features are validated by support vector machine and Naive Bayes classifier. Finally the performance measures of the two classifiers are compared.
机译：眼内的内部压力的增加导致光学神经的降解，这导致青光眼。它是一种眼部疾病，在发生一些视觉损失之前，不会检测到早期症状。因此，青光眼的诊断是最重要的，最大限度地减少视力丧失的风险。在本文中，通过使用主成分分析来增强输入视网膜图像，并且通过Gabor滤波器，形态操作和阈值技术去除血管。使用图像的纹理特征来执行青光眼图像分类。使用2-D离散小波变换（DWT）获得纹理特征。本文中使用的过滤器是Symlet3（Sym3）和双正交（Bio3.3，Bio3.5）。提取的特征是通过支持向量机和朴素贝叶斯分类器验证的。最后比较了两个分类器的性能测量。

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