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オゴハラ カズノリ
OGOHARA Kazunori
小郷原 一智 所属 京都産業大学 理学部 宇宙物理・気象学科 職種 准教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2016 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | Automated blood vessel extraction using local features on retinal images |
| 執筆形態 | その他 |
| 掲載誌名 | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
| 出版社・発行元 | SPIE |
| 巻・号・頁 | 9785 |
| 著者・共著者 | Yuji Hatanaka,Kazuki Samo,Mikiya Tajima,Kazunori Ogohara,Chisako Muramatsu,Susumu Okumura,Hiroshi Fujita |
| 概要 | 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. |
| DOI | 10.1117/12.2216572 |
| ISSN | 1605-7422 |