Paper Abstract and Keywords |
Presentation |
2016-08-26 15:20
Automated retinal blood vessel extraction based on black-hat transformation and high-order local autocorrelation Yuji Hatanaka, Kazuki Samo, Kazunori Ogohara (Univ. Shiga Prefecture), Chisako Muramatsu (Gifu Univ.), Wataru Sunayama (Univ. Shiga Prefecture), Hiroshi Fujita (Gifu Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
An automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images is presented. The blood vessels were enhanced by black-hat transformation as a pre-processing. One hundred five HLAC features were calculated in a black-hat filtered image, and they were inputted into a first layer of an artificial neural network (ANN). The output, a green component of the color retinal image, output values of three kinds of filter were then inputted into a second layer of ANN. Using DRIVE database, the area under the curve (AUC) based on ROC analysis was 0.960 as a result of our study. The result can be used for the blood vessels extraction. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Blood vessel extraction / High-order local autocorrelation / Hypertensive change / Retinal image / Segmentation / / / |
Reference Info. |
ITE Tech. Rep., vol. 40, pp. 27-30, Aug. 2016. |
Paper # |
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Date of Issue |
2016-08-19 (AIT) |
ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
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