A fast, efficient and automated method to extract vessels from fundus images

• Chanwimaluang T, Fan G (2003) An efficient blood vessel detection algorithm for retinal images using local entropy thresholding, vol 5. In: Proceedings of the International Symposium on Circuits and Systems, 2003. ISCAS ‘03. V-21–V-24

• Chaohong W (2007) Advanced feature extraction algorithms for automatic fingerprint recognition systems. PhD dissertation, Faculty of the Graduate School of State University, Buffalo, New York

• Chaudhuri S, Chatterjee S, Katz N, Nelson N, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269

  Article  Google Scholar 

• Gonzalez R, Woods J (2008) Digital image processing, 3rd edn. Prentice Hall, New Jersey

• Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image segmentation. IEEE Trans Syst Man Cybern, SMC-3, (6):610–621

• HongQing Z (2004) Segmentation of blood vessels in retinal images using 2D entropies of gray level-gradient co-occurrence matrix, vol 3, issue 17–21. In: IEEE international conference acoustics, speech, and signal processing (ICASSP), pp 509–512

• Hu J, Kahsi R, Lopresti D, Nagy G, Wilfong G (2001) Why table ground-truthing is hard. In: Proceedings of the sixth international conference on document analysis and recognition, ICDAR-01, pp 129-133

• Jiang X, Mojon D (2003) Adaptive local thresholding by verification-based multi-threshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Recogn Anal Mach Intell 25:131–137

  Article  Google Scholar 

• Jung E, Hong K (2006) Automatic retinal vasculature structure tracing and vascular landmark extraction from human eye image. In: Proceedings of the international conference on hybrid information technology, ICHIT, IEEE Computer Society, pp 161–167

• Kirbas C, Quek F (2004) A review of vessel extraction techniques and algorithms, ACM Computing Surveys, 36(2):81–121

• Kriston-Vizi J, Chinta R, Puah WC, Tiehua D, Wasser M (2006) An ImageJ plugin for the benchmarking of 3D segmentation algorithms and the specification of ground truth dataset. Imaging Informatics Division, Bioinformatics Institute Agency for Science, Technology and Research (A*STAR), Singapore

• Martinez-Perez M, Hughes A, Stanton A, Thom S, Bharath A, Parker K (1999) Scale-space analysis for the characterization of retinal blood vessels. In: Taylor C, Colchester A (eds) Medical image computing and computer-assisted intervention-MICCAI’99, pp 90–97

• Niemeijer M, Staal JJ, van Ginneken B, Loog M, Abramoff MD (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Michael Fitzpatrick J, Sonka M (eds) Proceedings of SPIE on SPIE medical imaging, vol 5370, 648, pp 648–656

• Niessen W, Bouma C, Vincken K, Viergever M (2000) Error metrics for quantitative evaluation of medical image segmentation. In: Klette R, Stiehl H, Viergever M, Vincken K (eds) Performance characterization in computer vision. Kluwer, The Netherlands, pp 275–284

  Google Scholar 

• Pal NR, Pal SK (1989) Entropic thresholding. Signal Process 16:97–10

  Google Scholar 

• Siddalingaswamy PC, Prabhu GK (2007) Automated detection of anatomical structures in retinal images. In: International conference on computational intelligence and multimedia applications, vol 3, pp 164–168

• Soares JVB, Leandro JJG, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 25:1214–1222

  Article  Google Scholar 

• Srivastavas V (2005) Performance of micro-calcification detection algorithms, Master thesis, Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA

• Staal J, Abramoff M, Niemeijer M, Viergever M, van Ginneken B (2004) Ridge-based Vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23:501–509

  Article  Google Scholar 

• Wang L, Bhalerao A (2003) Model based segmentation for retinal fundus images. In: Proceedings of Scandinavian conference on image analysis (SCIA)

• Yang CW, Dye M, Shuenn C, Chuin W, Chia W, Chien L, Pau C, Chein C (2000) Computer-aided diagnostic detection system of venous beading in retinal images. Society of photo-optical instrumentation engineers. Opt Eng, 39(1293):1293–1303

  Google Scholar 

• Zana F, Klein JC (1977) Robust segmentation of vessels from retinal angiography. In: International conference on digital signal processing. Santorini, Greece, pp 1087–1091

• Zhang YF, Zhang Y (2006) Another method of building 2D entropy to realize automatic segmentation. International Symposium on Instrumentation Science and Technology. J Phys: Conf Ser 48:303–307

  Google Scholar 

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