论文标题
通过基于图的统计特征，通过质量改进的图像进行小波神经网络进行青光眼分类的新型方法
A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images
论文作者
论文摘要
在本文中，我们提出了一种新的青光眼分类方法，该方法在最佳增强的视网膜图像特征上采用小波神经网络（WNN）。为了避免眼科医生对视网膜图像进行乏味和错误的手动分析，计算机辅助诊断（CAD）实质上有助于强大的诊断。我们的目标是以一种新的方法引入CAD系统。视网膜图像质量改进尝试分为两个阶段。视网膜图像预处理阶段通过基于分位数的直方图修饰来改善图像的亮度和对比度。其次是图像增强阶段，该阶段涉及使用图像特异性动态结构元素以进行视网膜结构富集。基于图形的视网膜图像特征在本地图结构（LGS）和图形最短路径（GSP）统计信息以及增强视网膜数据集的统计特征以及统计特征中提取。 WNN被用来用合适的小波激活函数对青光眼视网膜图像进行分类。将WNN分类器的性能与具有各种数据集的多层感知器神经网络进行了比较。结果表明，我们的方法优于现有方法。
In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis. Our objective is to introduce a CAD system with a fresh approach. Retinal image quality improvement is attempted in two phases. The retinal image preprocessing phase improves the brightness and contrast of the image through quantile based histogram modification. It is followed by the image enhancement phase, which involves multi scale morphological operations using image specific dynamic structuring elements for the retinal structure enrichment. Graph based retinal image features in terms of Local Graph Structures (LGS) and Graph Shortest Path (GSP) statistics are extracted from various directions along with the statistical features from the enhanced retinal dataset. WNN is employed to classify glaucoma retinal images with a suitable wavelet activation function. The performance of the WNN classifier is compared with multilayer perceptron neural networks with various datasets. The results show our approach is superior to the existing approaches.
