Visual Computing and Image Processing Lab
Oklahoma State University

Imaging, Processing, Inferencing and Learning



Retinal Blood Vessel Extraction

We have studied feature extraction for both anatomical structures, such as optic disc and blood vessels, as well as various pathological lesions associated with Diabetic Retinopathy (DR). This feature extraction research is very valuable to improve the efficiency and consistency of retinal image evaluation that is a subjective, costly, and labor-intensive process. Specifically, we have developed an efficient local entropy-based thresholding approach to extract blood vessels from retinal images. It is shown that the our algorithm outperforms the other two thresholding methods. Compared with several recent blood vessel segmentation algorithms, the performance our algorithm is competitive but at a lower computational load. The source code of Matlab functions for vascular tree extraction is available with more demos.

Related Publications

  • T. Chanwimaluang and G. Fan, "Hybrid Retinal Image Registration", IEEE Trans. on Information Technology in Biomedicine, Vol. 10, No. 1, pp129-142, Jan. 2006. (Corrections)

  • T. Chanwimaluang and G. Fan, "An Efficient Algorithm for Extraction of Anatomical Structures in Retinal Images" , in Proc. IEEE International Conference on Image Processing, Barcelona, Spain, September 2003.

  • T. Chanwimaluang and G. Fan, "An Efficient Blood Vessel Detection Algorithm for Retinal Images using Local Entropy Thresholding", in Proc. of the 2003 IEEE International Symposium on Circuits and Systems, Bangkok, Thailand, May 25-28, 2003.


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