Unsupervised Segmentation of Retinal Blood Vessels Using the Human Visual System Line Detection Model
Subject Areas : Image Processing
Mohsen
Zardadi
1
(University of Birjand)
Nasser
Mehrshad
2
(University of Birjand)
Seyyed Mohammad
Razavi
3
(Birjand)
Keywords: Retinal Vessel Segmentation , Simple cell Model , DRIVE Database , STARE Database,
Abstract :
Retinal image assessment has been employed by the medical community for diagnosing vascular and non-vascular pathology. Computer based analysis of blood vessels in retinal images will help ophthalmologists monitor larger populations for vessel abnormalities. Automatic segmentation of blood vessels from retinal images is the initial step of the computer based assessment for blood vessel anomalies. In this paper, a fast unsupervised method for automatic detection of blood vessels in retinal images is presented. In order to eliminate optic disc and background noise in the fundus images, a simple preprocessing technique is introduced. First, a newly devised method, based on a simple cell model of the human visual system (HVS) enhances the blood vessels in various directions. Then, an activity function is presented on simple cell responses. Next, an adaptive threshold is used as an unsupervised classifier and classifies each pixel as a vessel pixel or a non-vessel pixel to obtain a vessel binary image. Lastly, morphological post-processing is applied to eliminate exudates which are detected as blood vessels. The method was tested on two publicly available databases, DRIVE and STARE, which are frequently used for this purpose. The results demonstrate that the performance of the proposed algorithm is comparable with state-of-the-art techniques.
[1] B. Bowling, Kanski's Clinical Ophthalmology: A Systematic Approach, Eighth ed. Sydney, Australia: Elsevier Health Science, 2015.
#[2] M. Esmaeili, H. Rabbani, A. Dehnavi, and A. Dehghani, "Automatic detection of exudates and optic disk in retinal images using curvelet transform," IET image processing, vol. 6, pp. 1005-1013, 2012.
#[3] S. W. Franklin and S. E. Rajan, "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images," IET Image Processing, vol. 8, pp. 1-9, 2014.
#[4] M. J. Fowler, "Microvascular and macrovascular complications of diabetes," Clinical Diabetes, vol. 26, pp. 77-82, 2008.
#[5] J. Anitha, C. K. S. Vijila, and D. J. Hemanth, "An Overview of Computational Intelligence Techniques for Retinal Disease Identification Applications," International Journal of Reviews in Computing, vol. 5, pp. 29-46, 2009.
#[6] M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, et al., "An ensemble classification-based approach applied to retinal blood vessel segmentation," Biomedical Engineering, IEEE Transactions on, vol. 59, pp. 2538-2548, 2012.
#[7] A. Hoover, V. Kouznetsova, and M. Goldbaum, "Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response," Medical Imaging, IEEE Transactions on, vol. 19, pp. 203-210, 2000.
#[8] L. Gang, O. Chutatape, and S. M. Krishnan, "Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter," Biomedical Engineering, IEEE Transactions on, vol. 49, pp. 168-172, 2002.
#[9] M. G. Cinsdikici and D. Aydın, "Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm," Computer methods and programs in biomedicine, vol. 96, pp. 85-95, 2009.
#[10] M. A. Amin and H. Yan, "High speed detection of retinal blood vessels in fundus image using phase congruency," Soft Computing, vol. 15, pp. 1217-1230, 2011.
#[11] F. Zana and J.-C. Klein, "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation," Image Processing, IEEE Transactions on, vol. 10, pp. 1010-1019, 2001.
#[12] M. Fraz, S. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, et al., "An approach to localize the retinal blood vessels using bit planes and centerline detection," Computer methods and programs in biomedicine, 2011.
#[13] A. M. Mendonca and A. Campilho, "Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,"Medical Imaging, IEEE Transactions on, vol. 25, pp. 1200-1213, 2006.
#[14] B. S. Lam and H. Yan, "A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields," Medical Imaging, IEEE Transactions on, vol. 27, pp. 237-246, 2008.
#[15] B. S. Lam, Y. Gao, and A.-C. Liew, "General retinal vessel segmentation using regularization-based multiconcavity modeling," Medical Imaging, IEEE Transactions on, vol. 29, pp. 1369-1381, 2010.
#[16] B. Al-Diri, A. Hunter, and D. Steel, "An active contour model for segmenting and measuring retinal vessels," Medical Imaging, IEEE Transactions on, vol. 28, pp. 1488-1497, 2009.
#[17] G. Gardner, D. Keating, T. Williamson, and A. Elliott, "Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool," British journal of Ophthalmology, vol. 80, pp. 940-944, 1996.
#[18] C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, "Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images," British Journal of Ophthalmology, vol. 83, pp. 902-910, 1999.
#[19] M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff, "Comparative study of retinal vessel segmentation methods on a new publicly available database," in Medical Imaging 2004, pp. 648-656.
#[20] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, "Ridge-based vessel segmentation in color images of the retina," Medical Imaging, IEEE Transactions on, vol. 23, pp. 501-509, 2004.
#[21] J. V. Soares, J. J. Leandro, R. M. Cesar, H. F. Jelinek, and M. J. Cree, "Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification," Medical Imaging, IEEE Transactions on, vol. 25, pp. 1214-1222, 2006.
#[22] D. Marín, A. Aquino, M. E. Gegúndez-Arias, and J. M. Bravo, "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features," Medical Imaging, IEEE Transactions on, vol. 30, pp. 146-158, 2011.
#[23] J. P. Jones and L. A. Palmer, "An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex," Journal of Neurophysiology, vol. 58, pp. 1233-1258, 1987.
#[24] D. G. Albrecht and W. S. Geisler, "Motion selectivity and the contrast-response function of simple cells in the visual cortex," Visual neuroscience, vol. 7, pp. 531-546, 1991.
#[25] D. C. Somers, S. B. Nelson, and M. Sur, "An emergent model of orientation selectivity in cat visual cortical simple cells," The Journal of neuroscience, vol. 15, pp. 5448-5465, 1995.
#[26] D. Ferster, S. Chung, and H. Wheat, "Orientation selectivity of thalamic input to simple cells of cat visual cortex," Nature, vol. 380, pp. 249-252, 1996.
#[27] C. Grigorescu, N. Petkov, and M. A. Westenberg, "Contour detection based on nonclassical receptive field inhibition," Image Processing, IEEE Transactions on, vol. 12, pp. 729-739, 2003.
#[28] D. H. Hubel and T. N. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat's visual cortex," The Journal of physiology, vol. 160, p. 106, 1962.
#[29] J. G. Daugman, "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters," Optical Society of America, Journal, A: Optics and Image Science, vol. 2, pp. 1160-1169, 1985.
#[30] Research Section, Digital Retinal Image for Vessel Extraction(DRIVE) Database. Utrecht, The Netherlands, Univ. Med. Center Utrecht, Image Sci. Inst. [Online]. Available: http://www.isi.uu.nl/Research/Databases/DRIVE/[Feb. 1, 2016]
#[31] STARE database, STARE ProjectWebsite. Clemson, SC, Clemson Univ. [Online]. Available: http://www.ces.clemson.edu/~ahoover/stare/[Feb. 1, 2016]
#[32] X. Jiang and D. Mojon, "Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 25, pp. 131-137, 2003.
#[33] X. You, Q. Peng, Y. Yuan, Y.-m. Cheung, and J. Lei, "Segmentation of retinal blood vessels using the radial projection and semi-supervised approach," Pattern Recognition, vol. 44, pp. 2314-2324, 2011.