Improving dense conditional random field for retinal vessel segmentation by discriminative feature learning and thin-vessel enhancement.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 24, 2017

Abstract

BACKGROUND AND OBJECTIVES: As retinal vessels in color fundus images are thin and elongated structures, standard pairwise based random fields, which always suffer the "shrinking bias" problem, are not competent for such segmentation task. Recently, a dense conditional random field (CRF) model has been successfully used in retinal vessel segmentation. Its corresponding energy function is formulated as a linear combination of several unary features and a pairwise term. However, the hand-crafted unary features can be suboptimal in terms of linear models. Here we propose to learn discriminative unary features and enhance thin vessels for pairwise potentials to further improve the segmentation performance.

Authors

  • Lei Zhou
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Qi Yu
    Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
  • Xun Xu
    BGI-Shenzhen, Shenzhen 518083, China.
  • Yun Gu
    Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, SEIEE Building 2-427, No. 800, Dongchuan Road, Minhang District, Shanghai, 200240 China.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.

Keywords

External Resources

Popular Topics
Recent Journals