Paper Info
Title
Accurate detection of blood vessels improves the detection of exudates in color fundus images.
Abstract
Exudates are one of the earliest and most prevalent symptoms of diseases leading to blindness such as diabetic retinopathy and macular degeneration. Certain areas of the retina with such conditions are to be photocoagulated by laser to stop the disease progress and prevent blindness. Outlining these areas is dependent on outlining the lesions and the anatomic structures of the retina. In this paper, we provide a new method for the detection of blood vessels that improves the detection of exudates in fundus photographs. The method starts with an edge detection algorithm which results in a over segmented image. Then the new feature-based algorithm can be used to accurately detect the blood vessels. This algorithm considers the characteristics of a retinal blood vessel such as its width range, intensities and orientations for the purpose of selective segmentation. Because of its bulb shape and its color similarity with exudates, the optic disc can be detected using the common Hough transform technique. The extracted blood vessel tree and optic disc could be subtracted from the over segmented image to get an initial estimate of exudates. The final estimation of exudates can then be obtained by morphological reconstruction based on the appearance of exudates. This method is shown to be promising since it increases the sensitivity and specificity of exudates detection to 80% and 100% respectively.
Year
DOI
Venue
2012
10.1016/j.cmpb.2012.06.006
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
optic disc,segmented image,color fundus image,accurate detection,new feature-based algorithm,edge detection algorithm,blood vessel,exudates detection,retinal blood vessel,anatomic structure,blood vessel tree,new method,snakes,mathematical morphology
Diabetic retinopathy,Computer vision,Retina,Computer science,Edge detection,Mathematical morphology,Segmentation,Fundus (eye),Hough transform,Optic disc,Artificial intelligence
Journal
Volume
Issue
ISSN
108
3
1872-7565
Citations 
PageRank 
References 
14
0.72
9
Authors
2
Name
Order
Citations
PageRank
Doaa Youssef1140.72
Nahed H Solouma2252.39