Paper Info
Title
Automated blood vessel extraction using local features on retinal images.
Abstract
An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. Although many blood vessel extraction methods based on contrast have been proposed, a technique based on the relation of neighbor pixels has not been published. HLAC features are shift-invariant; therefore, we applied HLAC features to retinal images. However, HLAC features are weak to turned image, thus a method was improved by the addition of HLAC features to a polar transformed image. The blood vessels were classified using an artificial neural network (ANN) with HLAC features using 105 mask patterns as input. To improve performance, the second ANN (ANN2) was constructed by using the green component of the color retinal image and the four output values of ANN, Gabor filter, double-ring filter and black-top-hat transformation. The retinal images used in this study were obtained from the "Digital Retinal Images for Vessel Extraction" (DRIVE) database. The ANN using HLAC output apparent white values in the blood vessel regions and could also extract blood vessels with low contrast. The outputs were evaluated using the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis. The AUC of ANN2 was 0.960 as a result of our study. The result can be used for the quantitative analysis of the blood vessels.
Year
DOI
Venue
2016
10.1117/12.2216572
Proceedings of SPIE
Keywords
Field
DocType
Blood vessel extraction,Local feature,High-order local autocorrelation,Hypertensive retinopathy,Arteriolar narrowing,CAD,Retinal image,Segmentation
Computer vision,Receiver operating characteristic,Segmentation,Gabor filter,Retinal image,Artificial intelligence,Pixel,Retinal,Artificial neural network,Physics,Blood vessel
Conference
Volume
ISSN
Citations 
9785
0277-786X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Y Hatanaka127624.77
Kazuki Samo200.34
Mikiya Tajima300.68
Kazunori Ogohara401.69
Chisako Muramatsu531735.56
Susumu Okumura600.68
Hiroshi Fujita711824.65