Somasundaram K, Rajendran P Alli
Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul 624 622, India.
Department of Computer Science and Engineering, Velammal College of Engineering and Technology, Madurai, Tamil Nadu 625 009, India.
ScientificWorldJournal. 2015;2015:534045. doi: 10.1155/2015/534045. Epub 2015 Apr 7.
Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy. To categorize the exudates, Diabetic Fundus Image Recuperation (DFIR) method based on sliding window approach is developed in this work to select the features of optic cup in digital retinal fundus images. The DFIR feature selection uses collection of sliding windows with varying range to obtain the features based on the histogram value using Group Sparsity Nonoverlapping Function. Using support vector model in the second phase, the DFIR method based on Spiral Basis Function effectively ranks the diabetic retinopathy disease level. The ranking of disease level on each candidate set provides a much promising result for developing practically automated and assisted diabetic retinopathy diagnosis system. Experimental work on digital fundus images using the DFIR method performs research on the factors such as sensitivity, ranking efficiency, and feature selection time.
眼底图像被广泛应用于诊断不同类型的眼部疾病。现有的方法，如基于特征的黄斑水肿检测（FMED）和最优调整形态学算子（OAMO），分别有效地检测了眼底图像中渗出物的存在并确定了渗出物检测的真阳性率。这些机械检测到的渗出物在糖尿病视网膜病变检测系统中没有包含更详细的特征选择技术。为了对渗出物进行分类，本文基于滑动窗口方法开发了糖尿病眼底图像恢复（DFIR）方法，以选择数字视网膜眼底图像中视杯的特征。DFIR特征选择使用具有不同范围的滑动窗口集合，基于直方图值使用组稀疏非重叠函数来获取特征。在第二阶段使用支持向量模型，基于螺旋基函数的DFIR方法有效地对糖尿病视网膜病变疾病水平进行排序。每个候选集上疾病水平的排序为开发实际的自动化辅助糖尿病视网膜病变诊断系统提供了很有前景的结果。使用DFIR方法对数字眼底图像进行的实验工作对诸如灵敏度、排序效率和特征选择时间等因素进行了研究。
