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24 March 2016 Automated blood vessel extraction using local features on retinal images
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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.
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Yuji Hatanaka, Kazuki Samo, Mikiya Tajima, Kazunori Ogohara, Chisako Muramatsu, Susumu Okumura, and Hiroshi Fujita "Automated blood vessel extraction using local features on retinal images", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852F (24 March 2016);

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