AN EFFICIENT TECHNIQUE FOR RETINAL VESSEL SEGMENTATION AND DENOISING USING MODIFIED ISODATA AND CLAHE
Retinal damage caused due to complications of diabetes is known as Diabetic Retinopathy (DR). In this case, the vision is obscured due to the damage of retinal tinny blood vessels of the retina. These tinny blood vessels may cause leakage which affect the vision and can lead to complete blindness. Identification of these new retinal vessels and their structure is essential for analysis of DR. Automatic blood vessels segmentation plays a significant role to assist subsequent automatic methodologies that aid to such analysis. In literature most of the people have used computationally hungry a strong preprocessing steps followed by a simple thresholding and post processing, But in our proposed technique we utilize an arrangement of light pre-processing which consists of Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, a difference image of green channel from its Gaussian blur filtered image to remove local noise or geometrical object, Modified Iterative Self Organizing Data Analysis Technique (MISODATA) for segmentation of vessel and non-vessel pixels based on global and local thresholding, and a strong post processing using region properties (area, eccentricity) to eliminate the unwanted region/segment, non-vessel pixels and noise that never been used to reject misclassified foreground pixels. The strategy is tested on the publically accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases. The performance of proposed technique is assessed comprehensively and the acquired accuracy, robustness, low complexity and high efficiency and very less computational time that make the method an efficient tool for automatic retinal image analysis. Proposed technique perform well as compared to the existing strategies on the online available databases in term of accuracy, sensitivity, specificity, false positive rate, true positive rate and area under receiver operating characteristic (ROC) curve.
 Bernardes, Rui, Pedro Serranho, and Conceição Lobo. Digital ocular fundus imaging: a review. Ophthalmologica 226, no. 4 2011; 161-181.
 Erdt, Marius, Sebastian Steger, and Georgios Sakas. Regmentation: A new view of image segmentation and registration. Journal of Radiation Oncology Informatics 4, no. 1, 2012; 1-23.
 Miri, Mohammad Saleh, and Ali Mahloojifar. Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. Biomedical Engineering, IEEE Transactions on 58, no. 5, 2011; 1183-1192.
 Kanski, Jack J., and Brad Bowling. Synopsis of Clinical Ophthalmology. Elsevier Health Sciences, 2012.
 Li, Ying, Giovanni Gregori, Robert W. Knighton, Brandon J. Lujan, and Philip J. Rosenfeld. Registration of OCT fundus images with color fundus photographs based on blood vessel ridges. Optics express 19, no. 1, 2011; 7-16.
 Zwiggelaar, Reyer, Susan M. Astley, Caroline RM Boggis, and Christopher J. Taylor. Linear structures in mammographic images: detection and classification. Medical Imaging, IEEE Transactions on 23, no. 9, 2004; 1077-1086.
 Shabbir, Safia, Anam Tariq, and M. Usman Akram. A Comparison and Evaluation of Computerized Methods for Blood Vessel Enhancement and Segmentation in Retinal Images. International Journal of Future Computer and Communication 2.6, 2013; 600-603.
 Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG. Blood vessel segmentation methodologies in retinal images - a survey. Comput Meth Prog Bio. 2012; 108: 407–433. doi: 10.1016/j.cmpb.2012.03.009
 Azzopardi G, Strisciuglio N, Vento M, Petkov N. Trainable COSFIRE filters for vessel delineation with application to retinal images. Med Image Anal. 2015; 19: 46–57. doi: 10.1016/j.media.2014.08.002 PMID: 25240643
 Ricci E, Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imag. 2007; 26: 1357–1365. doi: 10.1109/TMI.2007.898551
 Becker, Carlos, Roberto Rigamonti, Vincent Lepetit, and Pascal Fua. Supervised feature learning for curvilinear structure segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013, Springer Berlin Heidelberg, 2013; 526-533.
 Sinthanayothin C, Boyce J, Cook H, Williamson T. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Brit J Ophthal. 1999; 83: 902–910. doi: 10.1136/bjo.83.8.902 PMID: 10413690
 Niemeijer M, Staal J, van Ginneken B, Loog M, Abramoff M. Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Proc SPIE Medical Imaging. 2004; 648-656.
 Staal J, Abramoff M, Niemeijer M, Viergever M, van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imag. 2004; 23: 501–509. doi: 10.1109/TMI.2004.825627.
 Soares J, Cree M. Retinal vessel segmentation using the 2D Gabor wavelet and supervised classification. IEEE Trans Med Imag. 2006; 25: 1214–1222. doi: 10.1109/TMI.2006.879967.
 Lam, Benson SY, Yongsheng Gao, and Alan Wee-Chung Liew. General retinal vessel segmentation using regularization-based multiconcavity modeling. Medical Imaging, IEEE Transactions. 2010; 29: 1369-1381.
 Joshi, Vinayak S., Joseph M. Reinhardt, Mona K. Garvin, and Michael D. Abramoff. Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks. PloS one. 2014; 9(2): e88061.
 Al-Diri B, Hunter A, Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Trans Med Imag. 2009; 28: 1488–1497. doi: 10.1109/TMI.2009.2017941.
 Zhang, Jingdan, Yingjie Cui, Wuhan Jiang, and Le Wang. Blood Vessel Segmentation of Retinal Images Based on Neural Network. In Image and Graphics, Springer International Publishing, 2015; 11-17.
 Lupaşcu, Carmen Alina, and Domenico Tegolo. Automatic unsupervised segmentation of retinal vessels using self-organizing maps and k-means clustering. Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer Berlin Heidelberg, 2011. 263-274.
 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 Imag. 1989; 25: 203–210.
 Hoover, Adam, Valentina Kouznetsova, and Michael Goldbaum. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. Medical Imaging, IEEE Transactions on 19, no. 3, 2000; 203-210.
 Chutatape, O., Liu Zheng, and S. M. Krishnan. Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters. In Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, vol. 6, 1998; 3144-3149.
 Mendonça A, Campilho AC. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imag. 2007; 25: 1200–1213.
 Zana, Frederic, and Jean-Claude Klein. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. Image Processing, IEEE Transactions on 10, no. 7, 2001; 1010-1019.
 Palomera-Pérez M, Martinez-Perez M, Benítez-Pérez H, Ortega-Arjona J. Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE Trans Inf Technol Biomed. 2010; 14: 500–506. doi: 10.1109/TITB.2009.2036604 PMID: 20007040.
 Martinez-Perez M, Hughes A, Thom S, Bharath A, Parker K. Segmentation of blood vessels from red free and fluorescein retinal images. Med Image Anal. 2007; 11: 47–61. doi: 10.1016/j.media.2006.11.004 PMID: 17204445.
 Fraz MM, Barman SA, Remagnino P, Hoppe A, Uyyanonvara B, Owen CG. An approach to localize the retinal blood vessels using bit planes and centerline detection. Comput Methods Programs Biomed. 2012; 108: 600–616. doi: 10.1016/j.cmpb.2011.08.009 PMID: 21963241.
 Bankhead P, McGeown J, Curtis T. Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE. 2012; 7: e32435. doi: 10.1371/journal.pone.0032435.
 Zuiderveld, Karel. Contrast limited adaptive histogram equalization. In Graphics gems IV. Academic Press Professional, Inc., 1994; 474-485.
 Ball, Geoffrey H., and David J. Hall. ISODATA, a novel method of data analysis and pattern classification. Stanford research inst Menlo Park CA, 1965.
 Gonzalez, Rafael C., and E. Richard. Woods. Digital image processing. Ed: Prentice Hall Press, 2002; ISBN 0-201-18075-8.
 Pizer S, Amburn E, Austin J, Cromartie AR, Geselowitz A, Greer T, et al. Adaptative histogram equalization and its variations. Comput Vis Graph Image Process. 1987; 39: 355–368. doi: 10.1016/S0734-189X(87)80186-X.
 Pisano, Etta D., Shuquan Zong, Bradley M. Hemminger, Marla DeLuca, R. Eugene Johnston, et al. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital imaging 11, no. 4, 1998; 193-200.
 J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B.van Ginneken, DRIVE public online database, http://www.isi.uu.nl/Research/Databases/DRIVE/.
 Hoover and M. Goldbaum, STARE public online database, http://www.ces.clemson.edu/∼ahoover/stare/.
 Nguyen U, Bhuiyan A, Laurence A, Ramamohanarao K. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn. 2013; 46: 703–715. doi: 10.1016/j.patcog.2012.08.009.
 Mapayi, Temitope, Serestina Viriri, and Jules-Raymond Tapamo. Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information. Computational and mathematical methods in medicine, 2015.
 Zhao, Yitian, Yonghuai Liu, Xiangqian Wu, Simon P. Harding, and Yalin Zheng. Retinal Vessel Segmentation: An Efficient Graph Cut Approach with Retinex and Local Phase. 2015; e0122332.
 Marin D, Aquino A, Gegundez-Arias M, Bravo J. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imag. 2011; 30: 146–158. doi: 10.1109/TMI.2010.2064333.
The IIUM Engineering journal follows the open access policy.