Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis

• Autorzy: GeethaRamani R. , Balasubramanian L.

Identyfikatory

DOI

10.1016/j.bbe.2015.06.004

• Języki publikacji: EN

Abstrakty

EN

Most of the retinal diseases namely retinopathy, occlusion etc., can be identified through changes exhibited in retinal vasculature of fundus images. Thus, segmentation of retinal blood vessels aids in detecting the alterations and hence the disease. Manual segmentation of vessels requires expertise. It is a very tedious and time consuming task as vessels are only a few pixels wide and extend almost throughout entire span of the fundus image. Employing computational approaches for this purpose would help in efficient retinal analysis. The methodology proposed in this work involves sequential application of image pre-processing, supervised and unsupervised learning and image post-processing techniques. Image cropping, color transformation and color channel extraction, contrast enhancement, Gabor filtering and halfwave rectification are sequentially applied during pre-processing stage. A feature vector is formed from the pre-processed images. Principal component analysis is performed on the feature vector. K-means clustering is executed on this outcome to group pixels as either vessel or non-vessel cluster. Out of the two groups, the identified non-vessel group undergoes an ensemble classification process employing root guided decision tree with bagging, while vessel group is left unprocessed as further processing might increase misclassifications of vessels as non-vessels. The resultant segmented image is formed through combining the results of clustering and ensemble classification process. The vessel segmented output from previous phase is post-processed through morphological techniques. The proposed technique is validated on images from publicly available DRIVE database. The proposed methodology achieves an accuracy of 95.36%, which is comparable with the existing blood vessel segmentation techniques.

Słowa kluczowe

EN

retina; blood vessels; Gabor filtering; classification; clustering; fundus image

PL

siatkówka oka; naczynia krwionośne; filtracja Gabora; grupowanie; obraz dna oka

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2016
• Tom: Vol. 36, no. 1
• Strony: 102--118
• Opis fizyczny: Bibliogr. 50 poz., rys., tab.

Twórcy

autor

GeethaRamani R.

• Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai, 600025, India

autor

Balasubramanian L.

• Department of Information Science and Technology, College of Engineering, Guindy, Anna University, Chennai, 600025, India

Bibliografia

• [1] Abràmoff MD, Garvin MK, Sonka M. Retinal imaging and image analysis. IEEE Trans Med Imaging 2010;1(3): 169–208.
• [2] Patton N, et al. Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 2006;25:99–127.
• [3] Franklin SW, Rajan SE. Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images. Biocybern Biomed Eng 2014;34:117–24.
• [4] Fraz MM, Remagnino P, Hoppe A, et al. Blood vessel segmentation methodologies in retinal images – a survey. Comput Methods Programs Biomed 2012;108(1):407–33.
• [5] Staal J, Abramoff MD, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004;23:501–9.
• [6] Niemeijer MJJ, Staal B, van Ginneken. et al. Comparative study on retinal vessel segmentation methods on a new publicly available database. SPIE 2004;648–56.
• [7] Soares JVB, Leandro.J.J.G., Cesar RM, et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imaging 2006;25:1214–22.
• [8] 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 moments invariants-based features. IEEE Trans Med Imaging 2011;30(1):146–58.
• [9] Xu L, Luo S. A novel method for blood vessel detection from retinal images. BioMed Eng Online 2010;9(14).
• [10] You X, Peng Q, Yuan Y, et al. Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognit 2011;44:2314–24.
• [11] Chaudhuri S, Chatterjee S, Katz N, et al. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans Med Imaging 1989;8:263–9.
• [12] Kande GB, Subbaiah PV, Savithri TS. Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. J Med Syst 2009;34:849–58.
• [13] Xiaoyi J, Mojon D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans Pattern Anal Mach Intell 2003;25:131–7.
• [14] Cinsdikici MG, Aydin D. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Comput Methods Programs Biomed 2009;96:85–95.
• [15] Zhang B, Zhang L, Zhang L, Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput Biol Med 2010;40:438–45.
• [16] Frangi A, Niessen W, Vincken K, Viergever M. Multiscale vessel enhancement filtering. Proc Med Image Comput 1998;1496:130–7.
• [17] Budai A, Bock R, Maler A, et al. Robust vessel segmentation in fundus image. Int J BioMed Imaging 2013;1–11.
• [18] Hannink J, Dutta R, Bekkers E. Crossing-preserving multiscale vesselness. Medical image computing and computer-assissted intervention. Lect Notes Comput Sci 2014;8674:603–10.
• [19] Chakraborti T, Chowdry AS. A Self-Adaptive Matched Filter for Retinal Blood Vessel Detection. Machine Vision and Applications. Berlin HeidelBerg: Springer Verlag; 2014.
• [20] Mendonca AM, Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 2006;25:1200–13.
• [21] Miri MS, Mahloojifar A. Retinal image analysis using curvelet transform and multistructural elements morphology by reconstruction. IEEE Trans Biomed Eng 2011;58:1183–92.
• [22] Fraz MM, Barman SA, Remagnino P, et al. An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed 2012;108(2):600–16.
• [23] Bankhead P, Scholfield CN, McGeown JG, Curtis TM. Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 2012;7(3):e32435.
• [24] Usman Akram M, Khan SA. Multilayered thresholding-based blood vessel segmentation for screening of diabetic retinopathy. Eng Comput 2013;29(2):165–73.
• [25] Martinez-Perez ME, Hughes AD, Stanton AV, et al. Retinal blood vessel segmentation by means of scale-space analysis and region growing. The Second International Conference on Medical Image Computing and Computer-Assisted Intervention. London, UK: Springer-Verlag; 1999. p. 90–7.
• [26] Martinez-Perez ME, Hughes AD, Thom SA, et al. Segmentation of blood vessels from red-free and fluorescein retinal images. Med Image Anal 2007;11:47–61.
• [27] Anzalone A, Bizzarri F, Parodi M, Storace M. A modular supervised algorithm for vessel segmentation in red-free retinal images. Comput Biol Med 2008;38:913–22.
• [28] Vlachos M, Dermatas E. Multi-scale retinal vessel segmentation using line tracking. Comput Med Imaging Graph 2009;34:213–27.
• [29] Saffarzadeh VM, Osareh A, Shadgar B. Vessel segmentation in retinal images using multi-scale line operator and K-means clustering. J Med Signals Sens 2014;4(2):122–9.
• [30] Zhou L, Rzeszotarski MS, Singerman LJ, Chokreff JM. The detection and quantification of retinopathy using digital angiograms. IEEE Trans Med Imaging 1994;13(4):619–26.
• [31] Can A, Shen H, Turner JN, et al. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans Inf Technol Biomed 1999;3(2):125–38.
• [32] Tolias Y, Panas SM. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans Med Imaging 1998;17(2):263–73.
• [33] Lam BSY, Yongsheng G, Liew AWC. General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Trans Med Imaging 2010;29:1369–81.
• [34] Espona L, Carreira MJ, Penedo MG, Ortega M. Retinal vessel tree segmentation using a deformable contour model. ICPR, 19th International Conference on Pattern Recognition. 2008. pp. 1–4.
• [35] Niemeijer M, Staal JJ, Ginneken Bv, Loog M, Abramoff MD. DRIVE: Digital Retinal Images for Vessel Extraction; 2004, http://www.isi.uu.nl/Research/Databases/DRIVE.
• [36] Brainard DH. Callibration of computer controlled color monitor. Color Res Appl 1989;14(1):23–34.
• [37] Geusebroek J, et al. Color invariance. IEEE Trans Pattern Anal Mach Intell 2001;23(2):1338–50.
• [38] Pizer SM, Philipi Amburn E, Austin JD, et al. Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 1987;39:355–68.
• [39] Fogel I, Sagi D. Gabor filters as texture discriminator. Biol Cybern 1989;61(2).
• [40] Jolliffe IT. Principal Component Analysis.ISBN 978-0-387-95442-4 Springer-Verlag; 1986. p. 487.
• [41] Nancy P, Geetha Ramani R. Discovery of patterns and evaluation of clustering algorithms in social network data (Facebook 100 universities) through data mining methods and techniques. Int J Data Min Knowl Manag Process (IJDKP) 2012;2(5).
• [42] Lloyd SP. Least squares quantization in PCM. IEEE Trans Inf Theory 1982;28:128–37.
• [43] Geetha Ramani R, Jacob SG. Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties. PLOS ONE 2013;8(2):e55401.
• [44] Geetha Ramani R, Balsubrmanian L. Multi-class classification for prediction of retinal diseases (retinopathy and occlusion) from fundus images. ICKM. 2013. pp. 122–34.
• [45] Geetha Ramani R, Balasubramanian L, Jacob SG. ROC analysis of classifiers in automatic detection of diabetic retinopathy using shape features of fundus images. International Conference on Advances in Computing, Communications and Informatics. 2013. pp. 66–72.
• [46] Geetha Ramani R, Balasubramanian L, Alaghu Meenal A. Decision tree variants (absolute random decision tree and root guided decision tree) for improved classification of data. Int J Appl Eng Res 2015;10(17):13190–5.
• [47] Breiman L. Bagging predictors. Mach Learn 1996;24(2):123–40.
• [48] Gonzalez RC. Digital Image Processing. 3rd ed. Prentice Hall; 2008.
• [49] Geetha Ramani R, Balasubramanian L, Jacob SG. Automatic prediction of diabetic retinopathy and glaucoma through image processing and data mining techniques. International Conference on Machine Vision and Image Processing. 2012. pp. 163–7.
• [50] Geetha Ramani R, Balasubramanian L, Jacob SG. Data mining method of evaluating classifier prediction accuracy in retinal data. IEEE International Conference on Computational Intelligence and Computing Research. 2012. pp. 426–9.

Uwagi

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