Ant Colony-based System for Retinal Blood Vessels Segmentation
The segmentation of retinal blood vessels in the eye funds images is crucial stage in diagnosing infection of diabetic retinopathy. Traditionally, the vascular network is mapped by hand in a time-consuming process that requires both training and skill. Automating the process allows consistency, and most importantly, frees up the time that a skilled technician or doctor would normally use for manual screening. Several studies were carried out on the segmentation of blood vessels in general, however only a small number of them were associated to retinal blood vessels. In this paper, an approach for segmenting retinal blood vessels is presented using only ant colony system. It uses eight features; four are based on gray-level and four are based on Hu moment-invariants. The features are directly computed from values of image pixels, so they take about 90 s in computation. The performance evaluation of this system is estimated by using classification accuracy. The presented approach accuracy is 90.28 % and its sensitivity is 74 %.
KeywordsSegmentation Retinal blood vessels Features extraction Ant colony system Moment-invariants Diabetic retinopathy
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- Morello CM. Etiology and natural history of diabetic retinopathy: an overview. Am J Health Syst Pharm.; 64 (17 Suppl. 12): S3-7 (2007).Google Scholar
- Gardner TW, Antonetti DA, Barber AJ, LaNoue KF, Levison SW. Diabetic retinopathy: more than meets the eye. Surv Ophthalmol.; 47 Suppl 2:S253-62 (2002).Google Scholar
- Vijayakumari V, Suriyanarayanan N. Survey on the Detection Methods of Blood Vessel in Retinal Images. Eur. J. Sci. Res. 68, 1, 83-92 (2012).Google Scholar
- Serrarbassa PD, Dias AF, Vieira MF. New concepts on diabetic retinopathy: neural versus vascular damage. Arq Bras Oftalmol.; 71(3):459-63 (2008).Google Scholar
- Barber AJ.A new view of diabetic retinopathy: a neurodegenerative disease of the eye. Prog Neuropsychopharmacol Biol Psychiatry.; 27(2):283-90 (2003).Google Scholar
- Khan MI, Shaikh H, Mohd A. Mansuri, A Review of Retinal Vessel Segmentation Techniques and Algorithms. Int. J. Comp. Tech. Appl. 2(5): 1140-1144 (2011).Google Scholar
- Goatman K, Charnley A, Webster L, Nussey S. Assessment of auto-mated disease detection in diabetic retinopathy screening using two-field photography. PLoS One.; 6(12): e27524 (2011).Google Scholar
- Verma K, Deep P, Ramakrishnan AG. Detection and classification of diabetic retinopathy using retinal images. Annual IEEE India Conference (INDICON); pp. 1-6. (2011). DOI: 10.1109/INDCON.2011.6139346.
- Jones S, Edwards RT. Diabetic retinopathy screening: a systematic review of the economic evidence. Diabet Med.; 27(3):249-56 (2010).Google Scholar
- Rodgers M, Hodges R, Hawkins J, Hollingworth W, Duffy S, McKib-bin M, Mansfield M, Harbord R, Sterne J, Glasziou P, Whiting P, Westwood M. Colour vision testing for diabetic retinopathy: a systematic review of diagnostic accuracy and economic evaluation. Health Technol Assess.; 13(60):1-160 (2009).Google Scholar
- Farley TF, Mandava N, Prall FR, Carsky C. Accuracy of primary care clinicians in screening for diabetic retinopathy using single-image retinal photography. Ann Fam Med.; 6(5):428-34 (2008).Google Scholar
- Bloomgarden ZT. Screening for and managing diabetic retinopathy: current approaches. Am J Health Syst Pharm.; 64 (17 Suppl 12):S8-14 (2007).Google Scholar
- Chew EY. Screening options for diabetic retinopathy. Curr Opin Oph-thalmol.; 17(6):519-22 (2006).Google Scholar
- Sinclair SH. Diabetic retinopathy: the unmet needs for screening and a review of potential solutions. Expert Rev Med Devices.; 3(3): 301-13 (2006).Google Scholar
- Xu J, Hu G, Huang T, Huang H, Chen B. Using multifocal ERG re-sponses to discriminate diabetic retinopathy.Doc Ophthalmol.; 112(3):201-7 (2006).Google Scholar
- Jin X, Guangshu H, Tianna H, Houbin H, Bin C. The Multifocal ERG in Early Detection of Diabetic Retinopathy. Conf Proc IEEE Eng Med Biol Soc.; 7:7762-5 (2005).Google Scholar
- Dorigo M, Gambardella LM. Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1): 53–66 (1997).Google Scholar
- Cinsdikici MG, Aydn D. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput Methods Programs Biomed. 96(2): 85-95 (2009).Google Scholar
- Hooshyar S, Khayati R. Retina Vessel Detection Using Fuzzy Ant Colony Algorithm. In Proc of Canadian Conference on Computer and Robot Vision (CRV), Ottawa, 239-244 (2010).Google Scholar
- Marin D, Aquino A, Gegundez-Arias ME, Bravo JM. A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Grey-Level and Moment Invariants-Based Features. IEEE Trans Med Imaging.; 30(1):146-58 (2011).Google Scholar
- Hu MK. Visual Pattern Recognition by Moment Invariants. IRE Trans. Inform. Theory. 8(2): 179–187 (1962).Google Scholar
- Hall MA. Correlation-based feature selection for discrete and numeric class machine learning. In Proc of 17th International Conference on Machine Learning, San Francisco, CA, 359–366 (2000) ISBN: 1-55860-707-2.Google Scholar
- Lupacu CA, Tegolo D, Trucco E. A Comparative Study on Feature Selection for Retinal Vessel Segmentation Using FABC. In Proc of the 13th International Conference on Computer Analysis of Images and Patterns (CAIP 2009), 655–662, Lecture Notes in Computer Science (LNCS) 5702, Springer-Verlag Berlin Heidelberg (2009). DOI: 10.1007/978-3-642-03767-2_80.
- Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging, 23(4): 501-509 (2004).Google Scholar