Abstract
Automatic extraction of blood vessels is an important step in computer-aided diagnosis in ophthalmology. The blood vessels have different widths, orientations, and structures. Therefore, the extracting of the proper feature vector is a critical step especially in the classifier-based vessel segmentation methods. In this paper, a new multi-scale rotation-invariant local binary pattern operator is employed to extract efficient feature vector for different types of vessels in the retinal images. To estimate the vesselness value of each pixel, the obtained multi-scale feature vector is applied to an adaptive neuro-fuzzy inference system. Then by applying proper top-hat transform, thresholding, and length filtering, the thick and thin vessels are highlighted separately. The performance of the proposed method is measured on the publicly available DRIVE and STARE databases. The average accuracy 0.942 along with true positive rate (TPR) 0.752 and false positive rate (FPR) 0.041 is very close to the manual segmentation rates obtained by the second observer. The proposed method is also compared with several state-of-the-art methods. The proposed method shows higher average TPR in the same range of FPR and accuracy.
Similar content being viewed by others
References
Chaudhuri S, Chatterjee S, Katz N, Nelson M, Goldbaum M (1989) Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 8(3):263–269
Hoover A, Kouznetsova V, Goldbaum M (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203–210
Niemeijer M, Staal JJ, VanGinneken B, Loog M, Abramoff MD (2004) Comparative study of retinal vessel segmentation methods on a new publicly available database, SPIEMed. Imaging 5370:648–656
Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32(8):720–727
Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4–5):198–211
Zana F, Klein JC (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 10(7):1010–1019
Matsopoulos GK, Asvestas PA, Delibasis KK, Mouravliansky NA, Zeyen TG (2008) Detection of glaucomatous change based on vessel shape analysis. Comput Med Imaging Graph 32(3):183–192
Lin T, Zheng Y (2003) Node-matching-based pattern recognition method for retinal blood vessel images. Opt Eng 42(11):3302–3306
Jiang X, Mojon D (2003) Adaptive local thresholding by verification based multi threshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 25(1):131–137
Zhang B, Zhang L, Zhang L, Karray F (2010) Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 40:438–445
Narasimha-Iyer H, Mahadevan V, Beach JM, Roysam B (2008) Improved detection of the central reflex in retinal vessels using a generalized dual-Gaussian model and robust hypothesis testing. IEEE Trans Inf Technol Biomed 12(3):406–410
Zhou L, Rzeszotarsk MS, Singerman LJ, Chokreff JM (1994) The detection and quantification of retinopathy using digital angiograms. IEEE Trans Med Imaging 13(4):619–626
Delibasis KK, Kechriniotis AI, Tsonos C, Assimakis N (2010) Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput Methods Programs Biomed. doi:10.1016/j.cmpb.2010.03.004
Adel M, Moussaoui A, Rasigni M, Bourennane S, Hamami L (2010) Statistica-based tracking technique for linear structures detection: application to vessel segmentation in medical images. IEEE Signal Process Lett 17(6):555–558
Vlachos M, Dermatas E (2010) Multi-scale retinal vessel segmentation using line tracking. Comput Med Imaging Graph 34(3):213–227
Perfetti R, Ricci E, Casali D, Costantini G (2007) Cellular neural networks with virtual template expansion for retinal vessel segmentation. IEEE Trans Circuits Syst II 54:141–145
Soares JVB, Leandro JJG, CesarJr RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans Med Imaging 25:1214–1222
Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, VanGinneken B (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509
Supot S, Thanapong C, Chuchart P, Manas S (2007) Automatic segmentation of blood vessels in retinal images based on Fuzzy K-Median clustering, in: Proceedings of the IEEE International Conference on Integration Technology. Shenzhen, China, pp 584–588
Garg S, Sivaswamy J, Chandra S (2007) Unsupervised curvature-based retinal vessel segmentation. In: Proceedings of the IEEE international symposium on bio-medical imaging pp 344–347
Mendonca AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25(9):1200–1213
Palomera-Perez MA, Martinez-Perez ME, Benitez-Perez H, Ortega-Arjona JL (2010) Parallel Multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans Inf Technol Biomed 14(2):500–506
Martinez-Perez ME, Hughes AD, Thom SA, Bharath AA, Parker KH (2007) Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 11(1):47–61
Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353
Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Upper Saddle River, Prentice Hall
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(3):665–685
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distribution. Pattern Recogn 29:51–59
Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
Nanni L, Lumini A (2008) Local binary patterns for a hybrid fingerprint matcher. Pattern Recogn 41:3461–3466
Lucieer A, Stein A, Fisher P (2005) Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty. Int J Remote Sens 26(14):2917–2936
Fathi A, Naghsh-Nilchi AR (2012) Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognit Lett 33:1093–1100
Otsu N (1979) A threshold selection method from Gray-Level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fathi, A., Naghsh-Nilchi, A.R. Integrating adaptive neuro-fuzzy inference system and local binary pattern operator for robust retinal blood vessels segmentation. Neural Comput & Applic 22 (Suppl 1), 163–174 (2013). https://doi.org/10.1007/s00521-012-1118-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-012-1118-8