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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 | 2018 |
| 形態種別 | 研究論文 |
| 査読 | 査読あり |
| 標題 | Automated blood vessel extraction based on high-order local autocorrelation features on retinal images |
| 執筆形態 | その他 |
| 掲載誌名 | Lecture Notes in Computational Vision and Biomechanics |
| 出版社・発行元 | Springer Netherlands |
| 巻・号・頁 | 27,pp.803-810 |
| 著者・共著者 | Yuji Hatanaka,Kazuki Samo,Kazunori Ogohara,Wataru Sunayama,Chisako Muramatsu,Susumu Okumura,Hiroshi Fujita |
| 概要 | Automated blood vessels detection on retinal images is an important process in the development of pathologies analysis systems. This paper describes about an automated blood vessel extraction using high-order local autocorrelation (HLAC) on retinal images. Although HLAC features are shift-invariant, HLAC features are weak to turned image. Therefore, a method was improved by the addition of HLAC features to a polar transformed image. We have proposed a method using HLAC, pixel-based-features and three filters. However, we have not investigated about feature selection and machine learning method. Therefore, this paper discusses about effective features and machine learning method. We tested eight methods by extension of HLAC features, addition of 4 kinds of pixel-based features, difference of preprocessing techniques, and 3 kinds of machine learning methods. Machine learning methods are general artificial neural network (ANN), a network using two ANNs, and Boosting algorithm. As a result, our already proposed method was the best. When the method was tested by using “Digital Retinal Images for Vessel Extraction” (DRIVE) database, the area under the curve (AUC) based on receiver operating characteristics (ROC) analysis was reached to 0.960. |
| DOI | 10.1007/978-3-319-68195-5_87 |
| ISSN | 2212-9413 |
| Put Code(ORCID) | 52343317 |
| PermalinkURL | http://www.scopus.com/inward/record.url?eid=2-s2.0-85032350688&partnerID=MN8TOARS |
| researchmap用URL | http://orcid.org/0000-0001-7666-4442 |