@article{TEXTUAL,
      recid = {9583},
      author = {Dai, Peishan and Luo, Hanyuan and Sheng, Hanwei and Zhao,  Yali and Li, Ling and Wu, Jing and Zhao, Yuqian and Suzuki,  Kenji},
      title = {A New Approach to Segment Both Main and Peripheral Retinal  Vessels Based on Gray-Voting and Gaussian Mixture Model},
      journal = {PLOS ONE},
      address = {2015-06-05},
      number = {TEXTUAL},
      abstract = {Vessel segmentation in retinal fundus images is a  preliminary step to clinical diagnosis for some systemic  diseases and some eye diseases. The performances of  existing methods for segmenting small vessels which are  usually of more importance than the main vessels in a  clinical diagnosis are not satisfactory in clinical use. In  this paper, we present a method for both main and  peripheral vessel segmentation. A local gray-level change  enhancement algorithm called gray-voting is used to enhance  the small vessels, while a two-dimensional Gabor wavelet is  used to extract the main vessels. We fuse the gray-voting  results with the 2D-Gabor filter results as pre-processing  outcome. A Gaussian mixture model is then used to extract  vessel clusters from the pre-processing outcome, while  small vessels fragments are obtained using another  gray-voting process, which complements the vessel cluster  extraction already performed. At the last step, we  eliminate the fragments that do not belong to the vessels  based on the shape of the fragments. We evaluated the  approach with two publicly available DRIVE (Staal et al.,  2004) and STARE (Hoover et at., 2000) datasets with  manually segmented results. For the STARE dataset, when  using the second manually segmented results which include  much more small vessels than the first manually segmented  results as the “gold standard,” this approach achieved an  average sensitivity, accuracy and specificity of 65.0%,  92.1% and 97.0%, respectively. The sensitivities of this  approach were much higher than those of the other existing  methods, with comparable specificities; these results thus  demonstrated that this approach was sensitive to detection  of small vessels.},
      url = {http://knowledge.uchicago.edu/record/9583},
}