title: Automated identification of retinal vessels using a multiscale directional contrast quantification (MDCQ) strategy.
abstract: OBJECTIVE
  A novel algorithm is presented to automatically identify the retinal vessels depicted in color fundus photographs.
  
  
  METHODS
  The proposed algorithm quantifies the contrast of each pixel in retinal images at multiple scales and fuses the resulting consequent contrast images in a progressive manner by leveraging their spatial difference and continuity. The multiscale strategy is to deal with the variety of retinal vessels in width, intensity, resolution, and orientation; and the progressive fusion is to combine consequent images and meanwhile avoid a sudden fusion of image noise and/or artifacts in space. To quantitatively assess the performance of the algorithm, we tested it on three publicly available databases, namely, DRIVE, STARE, and HRF. The agreement between the computer results and the manual delineation in these databases were quantified by computing their overlapping in both area and length (centerline). The measures include sensitivity, specificity, and accuracy.
  
  
  RESULTS
  For the DRIVE database, the sensitivities in identifying vessels in area and length were around 90% and 70%, respectively, the accuracy in pixel classification was around 99%, and the precisions in terms of both area and length were around 94%. For the STARE database, the sensitivities in identifying vessels were around 90% in area and 70% in length, and the accuracy in pixel classification was around 97%. For the HRF database, the sensitivities in identifying vessels were around 92% in area and 83% in length for the healthy subgroup, around 92% in area and 75% in length for the glaucomatous subgroup, around 91% in area and 73% in length for the diabetic retinopathy subgroup. For all three subgroups, the accuracy was around 98%.
  
  
  CONCLUSIONS
  The experimental results demonstrate that the developed algorithm is capable of identifying retinal vessels depicted in color fundus photographs in a relatively reliable manner.
authors.affiliation: National Engineering Research Center for Ophthalmic Equipments, Beijing, 100730 People's Republic of China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhen
authors.affiliation: Imaging Research Center, Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213.
authors.forename: Suicheng
authors.initials: S
authors.lastname: Gu
authors.affiliation: Imaging Research Center, Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213.
authors.forename: Xin
authors.initials: X
authors.lastname: Meng
authors.affiliation: National Engineering Research Center for Ophthalmic Equipments, Beijing, 100730 People's Republic of China.
authors.forename: Xinyuan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019.
authors.forename: Bin
authors.initials: B
authors.lastname: Zheng
authors.affiliation: National Engineering Research Center for Ophthalmic Equipments, Beijing, 100730 People's Republic of China.
authors.forename: Ningli
authors.initials: N
authors.lastname: Wang
authors.affiliation: Imaging Research Center, Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213.
authors.forename: Jiantao
authors.initials: J
authors.lastname: Pu
mesh_terms: D000465:Algorithms; D016477:Artifacts; D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000473:pathology; D005451:Fluorescein Angiography / Q000379:methods*; D005901:Glaucoma / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012171:Retinal Vessels / Q000033:anatomy & histology* / Q000473:pathology*; D012680:Sensitivity and Specificity