A comparative study on feature selection for retinal vessel segmentation using ant colony system

Advances in Intelligent Systems and Computing

Volumn 235, Issue , 2014, Pages 1-11

  Asad, Ahmed H ^a   Azar, Ahmad Taher ^b   Hassaanien, Aboul Ella Otifey ^c  

^a Cairo University   (Egypt)

^b BENHA UNIVERSITY   (Egypt)

^c CAIRO UNIVERSITY   (Egypt)

Author keywords

Ant Colony System; Computer aided diagnosis (CAD); Feature selection; Features Extraction; Retinal Blood Vessels; Segmentation

Indexed keywords

ANT COLONY OPTIMIZATION; BLOOD VESSELS; CLASSIFICATION (OF INFORMATION); COMPUTER AIDED DIAGNOSIS; DIAGNOSIS; DIGITAL STORAGE; EYE PROTECTION; IMAGE SEGMENTATION; OPHTHALMOLOGY;

ANT COLONY SYSTEM ALGORITHMS; ANT COLONY SYSTEMS; AUTOMATIC SEGMENTATIONS; COMPUTER AIDED DIAGNOSIS(CAD); FEATURES EXTRACTION; RETINAL BLOOD VESSELS; RETINAL VESSEL SEGMENTATIONS; TWO-CLASS CLASSIFICATION PROBLEMS;

FEATURE EXTRACTION;

EID: 84910633200     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-01778-5_1     Document Type: Article

References (39)

1. Assad, A., Azar, A.T., Hassaanien, A.E.: Ant Colony-based System for Retinal Blood Vessels Segmentation. In: Seventh International Conference on Bio-Inspired Computing: Theories and Application, 2012 (BIC-TA 2012), Gwalior, India, December 14 - 16 (2012)
2. Bloomgarden, Z.T.: Screening for and managing diabetic retinopathy: current approaches. Am. J. Health Syst. Pharm. 64 (17 suppl. 12), S8–S14 (2007)
3. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97(1-2), 245–271 (1997)
4. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software, CA, USA (1984)
5. Chew, E.Y.: Screening options for diabetic retinopathy. Curr. Opin. Ophthalmol 17(6), 519–522 (2006)
6. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning ap-proach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)
7. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images–a survey. Comput. Methods Programs Biomed. 108(1), 407–433 (2012), doi:10.1016/j.cmpb.2012.03.009.
8. Farley, T.F., Mandava, N., Prall, F.R., Carsky, C.: Accuracy of primary care clinicians in screening for diabetic retinopathy using single-image retinal photography. Ann. Fam Med. 6(5), 428–434 (2008)
9. Foracchia, M., Grisan, E., Ruggeri, A.: Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images. In: Book Abstracts 2nd International Workshop on Computer Assisted Fundus Image Analysis (2001)
10. 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)
11. Hall, M.A., Smith, L.A.: Feature Selection for Machine Learning: Comparing a Correlation- Based Filter Approach to the Wrapper. In: FLAIRS Conference, pp. 235–239 (1999)
12. Hall, M.A.: Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. In: ICML, pp. 359–366 (2000)
13. Hall, M., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transact. Knowl. Data Eng. 15(6), 1437–1447 (2003)
14. Hua, J.P., Tembe, W.D., Dougherty, E.R.: Performance of feature-selection methods in the classification of high-dimension data. Pattern Recognition 42(3), 409–424 (2009)
15. Hu, M.K.: Visual Pattern Recognition by Moment Invariants. IRE Trans. Inform. Theory. 8(2), 179–187 (1962)
16. 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, 7762–7765 (2005)
17. Jones, S., Edwards, R.T.: Diabetic retinopathy screening: a systematic review of the economic evidence. Diabet. Med. 27(3), 249–256 (2010)
18. Khan, M.I., Shaikh, H., Mansuri, A.M.: A Review of Retinal Vessel Segmentation Techniques and Algorithms. Int. J. Comp. Tech. Appl. 2(5), 1140–1144 (2011)
19. Kira, K., Rendell, L.A.: A practical approach to feature selection. In: The Proceedings of Ninth International Conference on Machine Learning, Aberdeen, Scotland, pp. 249–256. Morgan Kaufmann, Los Altos (1992)
20. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1- 2), 273–324 (1997)
21. Kononenko, I.: Estimating attributes: analysis and extensions of relief. In: Bergadano, F. (ed.) Proceedings of the Seventh European Conference on Machine Learning, vol. 784, pp. 171–182. Springer, Berlin (1994)
22. Leung, H., Wang, J.J., Rochtchina, E., Wong, T.Y., Klein, R., Mitchell, P.: Impact of current and past blood pressure on retinal arteriolar diameter in an older population. J. Hypertens 22(8), 1543–1549 (2004)
23. Marin, D., Aquino, A., Gegundez-Arias, M.E., Bravo, J.M.: 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), 146–158 (2011)
24. Mendonca, 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)
25. Mitchell, P., Leung, H., Wang, J.J., Rochtchina, E., Lee, A.J., Wong, T.Y., Klein, R.: Retinal vessel diameter and open-angle glaucoma: the Blue Mountains Eye Study. Ophthalmology 112(2), 245–250 (2005)
26. Morello, C.M.: Etiology and natural history of diabetic retinopathy: an overview. Am. J. Health Syst. Pharm. 64 (17 suppl. 12), S3–S7 (2007)
27. 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)
28. Serrarbassa, P.D., Dias, A.F., Vieira, M.F.: New concepts on diabetic retinopathy: neural versus vascular damage. Arq Bras Oftalmol. 71(3), 459–463 (2008)
29. Sinclair, S.H.: Diabetic retinopathy: the unmet needs for screening and a review of potential solutions. Expert Rev. Med. Devices 3(3), 301–313 (2006)
30. Soares, J.V., Leandro, J.J., Cesar Júnior, R.M., Jelinek, H.F., Cree, M.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006)
31. Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23(4), 501–509 (2004)
32. Talavera, L.: An evaluation of filter and wrapper methods for feature selection in categorical clustering. In: Proceeding of 6th International Symposium on Intelligent Data Analysis, Madrid, Spain, pp. 440–451 (2005)
33. Verma, K., Deep, P., Ramakrishnan, A.G.: Detection and classification of diabetic retinopathy using retinal images. In: Annual IEEE India Conference (INDICON), pp. 1–6 (2011), doi:10.1109/INDCON.2011.6139346
34. Vijayakumari, V., Suriyanarayanan, N.: Survey on the Detection Methods of Blood Vessel in Retinal Images. Eur. J. Sci. Res. 68(1), 83–92 (2012)
35. Wang, J.J., Taylor, B., Wong, T.Y., Chua, B., Rochtchina, E., Klein, R., Mitchell, P.: Retinal vessel diameters and obesity: a population-based study in older persons. Obesity (Silver Spring) 14(2), 206–214 (2006)
36. Xu, J., Hu, G., Huang, T., Huang, H., Chen, B.: Using multifocal ERG re-sponses to discriminate diabetic retinopathy. Doc. Ophthalmol. 112(3), 201–207 (2006)
37. You, X., Peng, Q., Yuan, Y., Cheung, Y., Lei, J.: Segmentation of retinal blood ves-sels using the radial projection and semi-supervised approach. Pattern Recognition 44(10- 11), 2314–2324 (2011)
38. Lupascu, C.A., Tegolo, D., Trucco, E.: A comparative study on feature selec-tion for retinal vessel segmentation using FABC. In: Proc 13th International Conference on Computer Analysis of Images and Patterns (CAIP), pp. 655–662 (September 2009)
39. Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: Retinal vessel segmentation using adaboost. IEEE Trans. Inf. Technol. Biomed. 14(5), 1267–1274 (2010)