Yi Zhen1, Suicheng Gu2, Xin Meng2, Xinyuan Zhang1, Bin Zheng3, Ningli Wang1, Jiantao Pu4. 1. National Engineering Research Center for Ophthalmic Equipments, Beijing, 100730 People's Republic of China. 2. Imaging Research Center, Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213. 3. School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma 73019. 4. Imaging Research Center, Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, 15213.
Abstract
PURPOSE: 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.
PURPOSE: 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: Joes Staal; Michael D Abràmoff; Meindert Niemeijer; Max A Viergever; Bram van Ginneken Journal: IEEE Trans Med Imaging Date: 2004-04 Impact factor: 10.048
Authors: Xiayu Xu; Meindert Niemeijer; Qi Song; Milan Sonka; Mona K Garvin; Joseph M Reinhardt; Michael D Abràmoff Journal: IEEE Trans Med Imaging Date: 2011-01-06 Impact factor: 10.048