眼底彩色图像中脓性渗出病灶的鉴别

Saima Waseem, M. Akram, Bilal Ashfaq Ahmed
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引用次数: 3

摘要

通过眼底图像进行眼部疾病的自动筛查和诊断已经在世界范围内实现。一种主要的视力丧失疾病被称为年龄相关性黄斑变性(AMD)有许多建议的自动筛查系统。这些系统检测到黄色明亮的病变,并通过病变的数量和大小将疾病分为晚期和早期。这些系统很难区分渗出物和脓肿,这是另一种与糖尿病视网膜病变相关的明亮病变。这两个病变在视网膜表面看起来很相似。区分这两种病变可以提高任何自动系统的性能。在本文中,我们提出了一种新的方法来区分这些病变。该方法包括两个阶段的程序。预处理后的第一阶段检测图像中的所有亮像素。从检测区域中去除可疑像素。在第二阶段,通过支持向量机(SVM)将明亮区域分类为积水和渗出。在公开数据集STARE上对该方法进行了评估。系统精度达到92%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drusen exudate lesion discrimination in colour fundus images
Automatic screening and diagnosis of ocular disease through fundus images are in place and considered worldwide. One of the leading sight loosing disease known as age related macular degeneration (AMD) has many proposed automatic screening systems. These systems detect yellow bright lesion and through the number of lesion and their size the disease is graded as advance and earlier stage. It becomes difficult for these systems to differentiate drusens from exudates another bright lesion associated with Diabetic retinopathy. These two lesions look similar on retinal surface. Differentiating these two lesions can improve the performance of any automatic system. In this paper we proposed a novel approach to discriminate these lesions. The approach consists of two stage procedure. The first stage after pre-processing detects all bright pixels from the image. The suspicious pixels are removed from the detected region. On the second stage bright regions are classified as drusen and exudates through Support Vector Machine (SVM). Proposed method was evaluated on publically available dataset STARE. The system achieve 92% accuracy.
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