Earticle

초록

영어

With the wealth of image data that is now becoming increasingly accessible through the advent of the world wide web and the proliferation of cheap, high quality digital cameras it is becoming ever more desirable to be able to automatically classify images into appropriate categories such that intelligent agents and other such intelligent software might make better informed decisions regarding them without a need for excessive human intervention. However, as with most Artificial Intelligence (A.I.) methods it is seen as necessary to take small steps towards your goal. With this in mind a method is proposed here to represent localised features using disjoint sub-images taken from several datasets of retinal images for their eventual use in an incremental learning system. A tile-based localised adaptive threshold selection method was taken for vessel segmentation based on separate colour components. Arteriole-venous differentiation was made possible by using the composite of these components and high quality fundal images. Performance was evaluated on the DRIVE and STARE datasets achieving average specificity of 0.9379 and sensitivity of 0.5924.

목차

Abstract
 1. Introduction
 2. Previous literature
  2.1. Retinal images
  2.2. Summary
 3. Methodology
  3.1. Illumination
 4. Threshold selection
  4.1. Connected components analysis
 5. Evaluation
  5.1. Assessment of segmentation
  5.2. The DRIVE dataset
  5.3. The STARE dataset
 6. Results
  6.1. The DRIVE dataset
  6.2. The STARE dataset
 7. Conclusions
 8. Further work
 9. References

저자

• Foon Chi Francis Chui [ University of Tasmania ]
• Ray Williams [ University of Tasmania ]
• Ivan Bindoff [ University of Tasmania ]
• Byeong Ho Kang [ University of Tasmania ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605