Comparative performance of Texton based vascular tree segmentation in retinal images

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


This paper considers the problem of vessel segmentation in optical fundus images of the retina. We adopt an approach that uses a machine learning paradigm to identify texture features called textons and present a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. Textons are generated by k-means clustering and texton maps representing vessels are derived by back-projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance.
Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing (ICIP)
PublisherThe Institute of Electrical and Electronics Engineers (IEEE)
Number of pages5
Publication statusPublished - 29 Jan 2015
EventIEEE International Conference on Image Processing (ICIP 2014) - Paris, France
Duration: 28 Oct 201430 Oct 2014


ConferenceIEEE International Conference on Image Processing (ICIP 2014)


  • Image Segmentation
  • Texton
  • Filter Bank
  • Clustering

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