Automated Blood Vessel Extraction Based on High-Order Local Autocorrelation Features on Retinal Images

  • Yuji HatanakaEmail author
  • Kazuki Samo
  • Kazunori Ogohara
  • Wataru Sunayama
  • Chisako Muramatsu
  • Susumu Okumura
  • Hiroshi Fujita
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 27)


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.


Blood vessel extraction High-order local autocorrelation Machine learning classifier Segmentation 



This research was supported by grants from the Telecommunications Advancement Foundation and JSPS KAKENHI, with grant numbers 16K01415 and 26108005, respectively.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Yuji Hatanaka
    • 1
    Email author
  • Kazuki Samo
    • 2
  • Kazunori Ogohara
    • 1
  • Wataru Sunayama
    • 1
  • Chisako Muramatsu
    • 3
  • Susumu Okumura
    • 4
  • Hiroshi Fujita
    • 3
  1. 1.Department of Electronic Systems Engineering, School of EngineeringThe University of Shiga PrefectureHikoneJapan
  2. 2.Division of Electronic Systems Engineering, Graduate School of EngineeringThe University of Shiga PrefectureHikoneJapan
  3. 3.Department of Intelligent Image Information, Graduate School of MedicineGifu UniversityGifuJapan
  4. 4.Department of Mechanical Systems Engineering, School of EngineeringThe University of Shiga PrefectureHikoneJapan

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