Abstract
In this paper a fast and accurate technique for retinal vessel tree extraction is proposed. It consists of a hybrid strategy based on global image filtering and contour tracing. With the aim of increasing the computation speed, the algorithm has been tailored for efficient execution on commodity graphics processing units achieving low execution times and high speedups over the CPU execution. The performance of the proposed method was tested on publicly available databases, STARE and DRIVE, based on standard measures such as accuracy, sensitivity and specificity. Results reveal an average accuracy comparable to that reported for state-of-the art techniques. Our method performs the vascular tree segmentation of the images in the DRIVE and the STARE databases in an average of 14 ms and 18 ms, respectively. To the best of our knowledge, the proposal features the highest accuracy/performance rate in the retinal blood vessel extraction domain.
Similar content being viewed by others
Notes
Code available in http://wiki.citius.usc.es/software/gpu-retina.
References
Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imaging 28(9), 1488–1497 (2009)
Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Comput. Biol. Med. 37, 262–267 (2007)
Alonso, C., Vilarino, D.L., Dudek, P., Penedo, M.G.: Fast retinal vessel tree extraction: a pixel-parallel approach. Int. J. Circuit Theory Appl. 36, 641–651 (2008)
Chanwimaluang, T., Fan, G.: An efficient algorithm for extraction of anatomical structures in retinal images. Int. Conf. Image Process. 1, 1093–1096 (2003)
Chaudhuri, S., Chaterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)
Dudek, P., Vilarino, D.L.: A cellular active contours algorithm based on region evolution. In: Proceedings of IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA2006, pp. 269–274 (2006)
Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., wen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images: a survey. In: Proceedings of Computer Methods and Programs in Biomedicine, vol. 108, pp. 407–433 (2012)
Fraz, M.M., Barman, S.A., Remagnino, P., Hoppe, A., Basit, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G.: An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput. Methods Progr. Biomed. 108, 600–616 (2012)
Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19, 203–210 (2000)
Krause, M., Alles, R.M., Burgeth, B., Weickert, J.: Fast retinal vessel analysis. J. Real-Time Image Process. (2013)
Liu, I., Sun, Y.: Recursive tracking on vascular networks in angiograms based on the detection-deletion scheme. IEEE Trans. Med. Imaging 12(2), 334–341 (1993)
Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: A new supervised method for bood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30, 146–158 (2011)
Martinez-Perez, M., Hughes, A., Stanton, A., Thorn, S., Bharath, A., Parker, K.: Scale-space analysis for the characterisation of retinal blood vessels. In: Proceedings of Medical Image Understanding, Analysis, pp. 57–60 (1999)
Mendoza, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006)
Narayanaswamy, A., Dwarakapuram, S., Bjornsson, C.S., Cutler, B.M., Shain, W., Roysam, B.: Robust adaptive 3D segmentation of vessel laminae from fluorescence confocal microscope images and parallel GPU implementation. IEEE Trans. Med. Imaging 29(3), 583–597 (2010)
Nieto, A., Brea, V., Vilarino, D.L., Osorio, R.R.: Performance analysis of massively parallel embedded hardware architectures for retinal image processing. EURASIP J. Image Video Process. 2011(10), 1–17 (2011)
NVIDIA Corporation: NVIDIA CUDA C Programming Guide 4.0, Santa Clara (2011)
Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Krger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Comput. Gr. Forum 26(1), 80–113 (2007)
Palomera-Perez, M.A., Martinez-Perez, M.E., Benitez-Perez, H., Ortega-Arjona, J.L.: Parallel multiscale feature extraction and region growing: application on retinal blood vessel detection. IEEE Trans. Inf. Technol. Biomed. 14, 500–506 (2007)
Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26, 1357–1365 (2007)
Soares, J.V.B., Leandro, J.J.G., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25, 1214–1222 (2006)
Staal, J., Abramoff, M.D., Niemeijer, M., Vierger, M.A., Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Stava, O., Benes, B.: Connected component labeling in CUDA. In: Hwu, W.W. (ed.) GPU Computing Gems, pp. 569–581 (2011)
Torres, G.S., Taborda, J.A.: Optic disk detection and segmentation of retinal images using an evolution strategy on GPU. In: Proceedings of XVIII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA), IEEE, pp. 1–5 (2013)
Vilarino, D.L., Rekezcky, C.: Pixel-level snakes on CNNUM: algorithm design on-chip implementation and applications. Int. J. Circuit Theory Appl. 33, 17–51 (2005)
You, X., Peng, Q., Yuan, Y., Cheung, Y., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit. 4(10–11), 2314–2324 (2011)
Zana, F., Klein, J.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10(7), 1010–1019 (2001)
Zhang, B., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first order derivative of Gaussian. Comput. Methods Progr. Biomed. 40(4), 438–445 (2010)
Acknowledgments
This work was supported in part by the Ministry of Science and Innovation, Government of Spain, and co-funded by European Union ERDF, under contract TIN 2010-17541.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Argüello, F., Vilariño, D.L., Heras, D.B. et al. GPU-based segmentation of retinal blood vessels. J Real-Time Image Proc 14, 773–782 (2018). https://doi.org/10.1007/s11554-014-0469-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11554-014-0469-z