Comparative performance of texton based vascular tree segmentation in retinal images

Zhang, Lei (2015) Comparative performance of texton based vascular tree segmentation in retinal images. In: 2014 IEEE International Conference on Image Processing (ICIP), 27-30th Oct 2014, Paris, France.

Full content URL: https://doi.org/10.1109/ICIP.2014.7025191

Documents
Comparative performance of texton based vascular tree segmentation in retinal images

Request a copy
[img] PDF
07025191.pdf - Whole Document
Restricted to Repository staff only

405kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

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.

Keywords:Image segmentation, Filter banks, Optical filters, Biomedical imaging
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
ID Code:35444
Deposited On:30 Apr 2019 15:14

Repository Staff Only: item control page