Automatic parameters selection of Gabor filters with the imperialism competitive algorithm with application to retinal vessel segmentation

• Autorzy: Farokhian F. , Yang C. , Demirel H. , Wu S. , Beheshti I.

Identyfikatory

DOI

10.1016/j.bbe.2016.12.007

• Języki publikacji: EN

Abstrakty

EN

Retinal images play an important role in the early diagnosis of diseases such as diabetes. In the present study, an automatic image processing technique is proposed to segment retinal blood vessels in fundus images. The technique includes the design of a bank of 180 Gabor filters with varying scale and elongation parameters. Furthermore, an optimization method, namely, the imperialism competitive algorithm (ICA), is adopted for automatic parameter selection of the Gabor filter. In addition, a systematic method is proposed to determine the threshold value for reliable performance. Finally, the performance of the proposed approach is analyzed and compared with that of other approaches on the basis of the publicly available DRIVE database. The proposed method achieves an area under the receiver operating characteristic curve of 0.953 and an average accuracy of up to 0.9392. Thus, the results show that the proposed method is well comparable with alternative methods in the literature.

Słowa kluczowe

EN

retinal image; Gabor filter; blood vessel detection; image segmentation; parameter selection

PL

obrazowanie siatkówki; filtr Gabora; naczynia krwionośne; segmentacja obrazu; wybór parametru

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 1
• Strony: 246--254
• Opis fizyczny: Bibliogr. 44 poz., rys., tab., wykr.

Twórcy

autor

Farokhian F.

• College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

autor

Yang C.

• College of Life Science and Bioengineering, Beijing University of Technology, Beijing, China

autor

Demirel H.

• Department of Electrical & Electronic Engineering, Eastern Mediterranean University, Gazimagusa, Mersin, Turkey

autor

Wu S.

• College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100022, China

autor

Beheshti I.

• Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, 4-1-1, Ogawahigashi-cho, Kodaira, Tokyo 187-8551, Japan

Bibliografia

• [1] Teng T, Lefley M, Claremont D. Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med Biol Eng Comput 2002;40:2–13.
• [2] Kanski JJ, Milewski SA, Damato B, Tanner V. Diseases of the ocular fundus. Elsevier; 2004.
• [3] Patton N, Aslam TMTM, MacGillivray T, Dearye IJ, Dhillon B, Eikelboom RH, et al. Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 2006;25(1): 99–127.
• [4] Lowell J, Hunter A, Steel D, Basu A, Ryder R, Kennedy RL. Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Trans Med Imaging 2004;23 (10):1196–204.
• [5] Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR. An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans Biomed Eng 2012;2538–48.
• [6] Intriago M, Uyaguari F, Jácome E. A review algorithms for retinal vesels segmentation. Latin Am J 2014;1(October).
• [7] Al-Diri B, Hunter A, Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imaging 2009;28:1488–97.
• [8] Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 1989;83:263–9.
• [9] Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 2000;19(3):203–10.
• [10] Staal J, Abràmoff MD, Niemeijer M, Viergever MA, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004;234: 501–9.
• [11] Jiang X, Mojon D. Adaptive local thresholding by verificationbased multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 2003;251:131–7.
• [12] Azzopardi G, Strisciuglio N, Vento M, Petkov N. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal 2015;19(1):46–57.
• [13] Strisciuglio N, Azzopardi G, Petkov N. Multiscale blood vessel delineation using B-COSFIRE filters. International Conference on Computer Analysis of Images and Patterns; 2015.
• [14] Bao X-R, Ge X, She L-H, Zhang S. Segmentation of retinal blood vessels based on cake filter. BioMed Res Int 2015;2015:1–11. http://dx.doi.org/10.1155/2015/137024.
• [15] Kharghanian R, Ahmadyfard A. Retinal blood vessel segmentation using gabor wavelet and line operator. Int J Mach Learn Comput 2012;2(5):593.
• [16] Chaudhari HP, Rahulkar AD, Patil CY. Segmentation of retinal vessels by the use of gabor wavelet and linear mean squared error classifier. Int J Emerg Eng Res Technol 2014;2 (2):119–25.
• [17] Kochner B, Schuhmann D, Michaelis M, Mann G, Englmeier KH. Course tracking and contour extraction of retinal vessels from color fundus photographs: most efficient use of steerable filters for model based image analysis. Proceedings of the SPIE, the International Society for Optical Engineering; February 1998. p. 755–61.
• [18] Can A, Shen H, Turner JN, Tanenbaum HL, Roysam B. Rapid automated tracing and feature extraction from retinal funduns image using direct exploratory algorithms. IEEE Trans Inf Technol Biomed 1999;3(June (2)):125–38.
• [19] Zana F, Klein JC. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 2001;10:1010–9.
• [20] Mendonca AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 2006;25(9):1200–13.
• [21] Soares JVB, Leandro JJG, Cesar Jr RM, Jelinek HF, Cree MJ. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 2006;259:1214–22.
• [22] Petkov N. Biologically motivated computationally intensive approaches to image pattern recognition. Future Gener Comput Syst 1995;11(4):451–65.
• [23] Rangayyan R, Ayres FJ. Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Biol Eng Comput 2006;44(October (10)):883–94.
• [24] DRIVE: Digital Retinal Images for Vessel Extraction; 2006, http://www.isi.uu.nl/Research/Databases/DRIVE/.
• [25] Siddalingaswamy PC, Gopalakrishna Prabhu K. Automatic detection of multiple oriented blood vessels in retinal images. Biomed Sci Eng 2010;3:101–7.
• [26] Rangayyan RM, Oloumi F, Oloumi F, Eshghzadeh-Zanjani P, Ayres FJ. Detection of blood vessels in the retina using Gabor filter. Proceedings of the 20th Canadian Conference on Electrical and Computer Engineering (CCECE 2007); 2007.
• [27] Zhu X, Rangayyan MR, Ells AL. Digital image processing for ophthalmology: detection of the optic nerve head; 2011.
• [28] Rangayyan FAaR. Performance analysis of oriented feature detectors. Proceedings of SIBGRAPI 2005: XVIII Brazilian Symposium on Computer Graphics and Image Processing; 2005.
• [29] Mudigonda NR, Rangayyan RM, Desautels JEL. Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE Trans Med Imaging 2001;20 (12):1215–27.
• [30] Zhu X, Rangayyan RM, Ells AL. Digital image processing for ophthalmology: detection of the optic nerve head; 2011.
• [31] Ayres FJ, Rangayyan RM. Performance analysis of oriented feature detector. Brazilian Symposium on Computer Graphics and Image Processing; 2005.
• [32] Heneghan C, Flynn J, O'Keefe M, Cahill M. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image; 2002.
• [33] Ayough A, Zandieh M, Farsijani H. GA and ICA approaches to job rotation scheduling problem: considering employee's boredom. Int J Adv Manuf Technol 2012;60:651–66.
• [34] Atashpaz-Gargari E, Lucas C. An algorithm for optimization inspires by imperialistic competition. IEEE Congress on Evolutionary Computation; 2007.
• [35] Mohammadi M, Tavakkoli-Moghaddam R, Rostami H. A multi-objective imperialist competitive algorithm for a capacitated hub covering location problem. Int J Ind Eng Comput 2011;2:671–88.
• [36] Moadi S, Mohaymany AS, Babaei M. Application of imperialist competitive algorithm to the emergency medical services location problem. Int J Artif Intell Appl 2011;2:137–47.
• [37] Ghanavati M, Gholamian MR, Minaei B, Davoudi M. An efficient cost function for imperialist competitive algorithm to find best clusters. J Theor Appl Inf Technol 2011;29:22–31.
• [38] Atashpaz-Gargari E, Lucas C. Designing an optimal PID controllr using Imperialist Competitive Algorithm. First Joint Congress on Fuzzy and Intelligent Systems Ferdowsi University of Mashhad; 2007.
• [39] Farokhian F, Demirel H. Blood vessels detection and segmentation in retina using Gabor filters. 10th IEEE High Capacity Optical Networks and Enabling Technologies (HONET-CNS); 2013.
• [40] Marin D, Aquino A, Gegundez-Arias ME, Bravo JM. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based feature. IEEE Trans Med Imaging 2011;30:146–58.
• [41] Palomera-Pérez MA, Martinez-Perez ME, Benitez-Perez H, Ortega-Arjona JL. Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans Inf Technol Biomed 2014;14.
• [42] Espona L, Carreira MJ, Ortega M, Penedo MG. A snake for retinal vessel segmentation. Conference on Pattern Recognition and Image Analysis; 2007.
• [43] Farokhian F, Demirel H. Fast detection and segmentation in retinal blood vessels using Gabor filters. 22nd Signal Processing and Communications Applications Conference (SIU); 2014.
• [44] Zhang B, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 2010;40.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).