@InProceedings{RodriguesBeze:2016:ReVeSe,
               author = "Rodrigues, Jardel das Chagas and Bezerra, Francisco Nivando",
                title = "Retinal Vessel Segmentation Using Parallel Grayscale 
                         Skeletonization Algorithm and Mathematical Morphology",
            booktitle = "Proceedings...",
                 year = "2016",
               editor = "Aliaga, Daniel G. and Davis, Larry S. and Farias, Ricardo C. and 
                         Fernandes, Leandro A. F. and Gibson, Stuart J. and Giraldi, Gilson 
                         A. and Gois, Jo{\~a}o Paulo and Maciel, Anderson and Menotti, 
                         David and Miranda, Paulo A. V. and Musse, Soraia and Namikawa, 
                         Laercio and Pamplona, Mauricio and Papa, Jo{\~a}o Paulo and 
                         Santos, Jefersson dos and Schwartz, William Robson and Thomaz, 
                         Carlos E.",
         organization = "Conference on Graphics, Patterns and Images, 29. (SIBGRAPI)",
            publisher = "IEEE Computer Society´s Conference Publishing Services",
              address = "Los Alamitos",
             keywords = "retinal blood vessel segmentation, mathematical morphology.",
             abstract = "Retinal vessel segmentation is an important step for the detection 
                         of numerous system diseases, such as glaucoma, diabetic 
                         retinopathy, and others. Thus, the retinal blood vessel analysis 
                         can be used to diagnose and to monitor the progress of these 
                         diseases. Manual segmentation of fundus images is a long and 
                         tedious task that requires a specialist. Therefore, many 
                         algorithms have been developed for this purpose. This paper 
                         demonstrates an automated method for retinal blood vessel 
                         segmentation based on the combination of topological and 
                         morphological vessel extractors. Each of these extractors is based 
                         on different blood vessel features to increase the detection 
                         robustness. The final segmentation is obtained intersecting the 
                         two resulting images, smoothing the vessel borders and removing 
                         spurious objects remaining. Our proposed method is tested on DRIVE 
                         and STARE databases, achieving an average accuracy of 0.9565 and 
                         0.9568, respectively, with good values of sensitivity and 
                         specificity.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP, Brazil",
      conference-year = "4-7 Oct. 2016",
                  doi = "10.1109/SIBGRAPI.2016.012",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2016.012",
             language = "en",
                  ibi = "8JMKD3MGPAW/3M5CARE",
                  url = "http://urlib.net/ibi/8JMKD3MGPAW/3M5CARE",
           targetfile = "PID4354727.pdf",
        urlaccessdate = "2024, Apr. 29"
}