.png){kind=icon}
返回
A novel method for retinal exudate segmentation using signal separation algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cmpb.2016.05.016.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Diabetic retinopathy is one of the major causes of blindness in the world. Early diagnosis of this disease is vital to the prevention of visual loss. The analysis of retinal lesions such as exudates, microaneurysms and hemorrhages is a prerequisite to detect diabetic disorders such as diabetic retinopathy and macular edema in fundus images. This paper presents an automatic method for the detection of retinal exudates. The novelty of this method lies in the use of Morphological Component Analysis (MCA) algorithm to separate lesions from normal retinal structures to facilitate the detection process. In the first stage, vessels are separated from lesions using the MCA algorithm with appropriate dictionaries. Then, the lesion part of retinal image is prepared for the detection of exudate regions. The final exudate map is created using dynamic thresholding and mathematical morphologies. Performance of the proposed method is measured on the three publicly available DiaretDB, HEI-MED and e-ophtha datasets. Accordingly, the AUC of 0.961 and 0.948 and 0.937 is achieved respectively, which are greater than most of the state-of-the-art methods. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
Keyword:
Exudate detection
Morphological component analysis (MCA) algorithm
Dynamic thresholding
Mathematical morphology
Diabetic retinopathy
Macula edema
期刊
IF:
4.8
论文数:
6.9K
被引数:
2.1W
机构
引用论文
Exudate detection in color retinal images for mass screening of diabetic retinopathy.png)
MEDICAL IMAGE ANALYSIS
OAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAI分享收藏
查看更多内容