International Journal of Advanced and Applied Sciences
Int. j. adv. appl. sci.
Print ISSN: 2313-626X
Volume 4, Issue 9 (September 2017), Pages: 156-160
Title: Wavelet filter techniques for segmenting retinal blood vessels
Author(s): Abdulsamad Al-Marghilnai 1, *, Romany F. Mansour 2
1College of Computer Science and Information, Northen Border University, Arar, Saudi Arabia
2Department of Computer Science, Faculty of Science, Northern Border University, Arar, Saudi Arabia
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Retinal fundus image is generally used to examine the diabetic retinopathy symptoms, by analysing blood vessel segmentation and also access the pathologies of the eye. Retinal blood vessel details can be mined from retinal fundus images through image processing. Processing involves three stages, Pre-processing, Segmentation, and Post-processing. Among the different segmentation algorithms existing, the wavelet filter method has been shown to be highly advantageous in distinguishing blood vessels effectively. Under this method, the objects in noisy background can be segmented and hence sort out the image from the background in a finer way. Retinal images obtained from retinal databases like DRIVE and STARE can be analysed using the wavelet filter algorithm. Herein wavelet filter method will be evaluated for efficient segmentation of the retinal blood vessels using retinal images obtained from databases.
© 2017 The Authors. Published by IASE.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Retinal image, Wavelet filter, Retinal blood vessels
Article History: Received 29 May 2017, Received in revised form 2 August 2017, Accepted 5 August 2017
Digital Object Identifier:
Al-Marghilnai A and Mansour RF (2017). Wavelet filter techniques for segmenting retinal blood vessels. International Journal of Advanced and Applied Sciences, 4(9): 156-160
- Akram MU, Atzaz A, Aneeque SF, and Khan SA (2009). Blood vessel enhancement and segmentation using wavelet transform. In the International Conference on Digital Image Processing, IEEE, Bangkok, Thailand: 34-38. https://doi.org/10.1109/ICDIP.2009.70
- Akram MU, Jamal I, Tariq A, and Imtiaz J (2012). Automated segmentation of blood vessels for detection of proliferative diabetic retinopathy. In the IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), IEEE, Hong Kong, China: 232-235. https://doi.org/10.1109/BHI.2012.6211553
- AOA (2014). Diabetes eye care of the patient with diabetes mellitus. American Optometric Association, Virginia, USA.
- Aras RA, Lestari T, Nugroho HA, and Ardiyanto I (2016). Segmentation of retinal blood vessels for detection of diabetic retinopathy: A review. Communications in Science and Technology, 1(1): 33-41.
- Chhabra S and Bhushan B (2014). Supervised pixel classification into arteries and veins of retinal images. In the Innovative Applications of Computational Intelligence on Power, Energy and Controls with their impact on Humanity, IEEE, Ghaziabad, India: 59-62. https://doi.org/10.1109/CIPECH.2014.7019098
- Cinsdikici MG and Aydın D (2009). Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Computer Methods and Programs in Biomedicine, 96(2): 85-95. https://doi.org/10.1016/j.cmpb.2009.04.005 PMid:19419790
- Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, and Barman SA (2012). Blood vessel segmentation methodologies in retinal images–a survey. Computer Methods and Programs in Biomedicine, 108(1): 407-433. https://doi.org/10.1016/j.cmpb.2012.03.009 PMid:22525589
- Gavlasová, A., Procházka, A., & Mudrová, M. (2006). Wavelet based image segmentation. In the 14th Annual Conference Technical Computing, Prague, Czech Republic. Available online at: http://www.humusoft.cz/ftp/www/papers/tcp06/gavlasova.pdf
- Han Z, Yin Y, Meng X, Yang G, Yan X (2014) Blood vessel segmentati on in pathological retinal image. In the IEEE International Conference on Data Mining Workshop, IEEE, Shenzhen, China: 960–967. https://doi.org/10.1109/ICDMW.2014.16
- Jadhav A and Patil BP (2015). Classification of diabetes retina images using blood vessel area. International Journal on Cybernetics and Informatics, 4(2): 251–257. https://doi.org/10.5121/ijci.2015.4224
- Joshi S and Karule PT (2012). Retinal blood vessel segmentation. International Journal of Engineering and Innovative Technology (IJEIT), 1(3): 175-178.
- Kauppi T, Kalesnykiene V, Kamarainen JK, Lensu L, Sorri I, Uusitalo H, and Pietilä J (2006). DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Machine Vision and Pattern Recognition Research Group, Lappeenranta University of Technology, Finland, Available online at: http://www.it.lut.fi/project/imageret/diaretdb0/doc/diaretdb0_techreport_v_1_1.pdf
- Knudtson MD, Klein BEK, Klein R, Wong TY, Hubbard LD, Lee KE, and Bulla CP (2004). Variation associated with measurement of retinal vessel diameters at different points in the pulse cycle. British Journal of Ophthalmology, 88(1): 57-61. https://doi.org/10.1136/bjo.88.1.57 PMid:14693774 PMCid:PMC1771926
- Kumar A, Gaur AK, and Srivastava M (2012). Segment based technique for detecting exudate from retinal fundus image. Procedia Technology, 6: 1–9. https://doi.org/10.1016/j.protcy.2012.10.001
- Lee THH (2006). Wavelet Analysis for Image Processing. Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan. Available online at: https://pdfs.semanticscholar.org/aff9/9f7e32890bb6843255e2bd6c0ff0092dfeab.pdf
- Li Q, You J, and Zhang D (2012). Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses. Expert Systems with Applications, 39(9): 7600-7610. https://doi.org/10.1016/j.eswa.2011.12.046
- Li Q, You J, Zhang L, and Bhattacharya P (2006). A multiscale approach to retinal vessel segmentation using Gabor filters and scale multiplication. In the IEEE International Conference on Systems, Man and Cybernetics (SMC'06), IEEE, Taipei, Taiwan: 4: 3521-3527. https://doi.org/10.1109/ICSMC.2006.384665
- Mansour R (2017). Evolutionary computing enriched computer aided diagnosis system for diabetic retinopathy: A survey. IEEE Reviews in Biomedical Engineering, IEEE Engineering in Medicine and Biology Society IEEE Consumer Electronics Society [Technical Co-Sponsor], PP(99): 1-1. https://doi.org/10.1109/RBME.2017.2705064
- Mansour RF (2012). Using genetic algorithm for identification of diabetic retinal exudates in digital color images. Journal of Intelligent Learning Systems and Applications, 4(03): 188-198. https://doi.org/10.4236/jilsa.2012.43019
- Mansour RF (2016). Iris recognition using gauss laplace filter. American Journal of Applied Sciences, 13(9): 962-968. https://doi.org/10.3844/ajassp.2016.962.968
- Mansour RF, Abdelrahim EM, and Al-Johani AS (2013). Identification of diabetic retinal exudates in digital color images using support vector machine. Journal of Intelligent Learning Systems and Applications, 5(03): 135-142. https://doi.org/10.4236/jilsa.2013.53015
- Martinez-Perez ME, Highes AD, Stanton AV, Thorn SA, Chapman N, Bharath AA, and Parker KH (2002). Retinal vascular tree morphology: A semi-automatic quantification. IEEE Transactions on Biomedical Engineering, 49(8): 912-917. https://doi.org/10.1109/TBME.2002.800789 PMid:12148830
- Mustafa WA, Yazid H, and Yaacob SB (2014). A review: Comparison between different type of filtering methods on the contrast variation retinal images. In the IEEE International Conference on Control System, Computing and Engineering, IEEE, Batu Ferringhi, Malaysia: 542-546. https://doi.org/10.1109/ICCSCE.2014.7072777
- Odstrcilik PIJ (2014). Analysis of retinal image data to support glaucoma diagnosis. Available online at: https://core.ac.uk/download/pdf/30311092.pdf
- Owen CG, Rudnicka AR, Nightingale CM, Mullen R, Barman SA, Sattar N, and Whincup PH (2011). Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; the child heart and health study in England (CHASE). Arteriosclerosis, Thrombosis, and Vascular Biology, 31(8): 1933-1938. https://doi.org/10.1161/ATVBAHA.111.225219 PMid:21659645 PMCid:PMC3145146
- Patankar SS, Mone AR, and Kulkarni JV (2013). Gradient features and optimal thresholding for retinal blood vessel segmentation. In the IEEE International Conference on Computational Intelligence and Computing Research, IEEE, Enathi, India: 1-5. https://doi.org/10.1109/ICCIC.2013.6724116
- Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, and Constable IJ (2006). Retinal image analysis: concepts, applications and potential. Progress in Retinal and Eye Research, 25(1): 99-127. https://doi.org/10.1016/j.preteyeres.2005.07.001 PMid:16154379
- Relan D, MacGillivray T, Ballerini L, and Trucco E (2014). Automatic retinal vessel classification using a least square-support vector machine in VAMPIRE. In the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Chicago, USA: 142-145. https://doi.org/10.1109/EMBC.2014.6943549
- Saleh MD, Eswaran C, and Mueen A (2011). An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection. Journal of Digital Imaging, 24(4): 564-572. https://doi.org/10.1007/s10278-010-9302-9 PMid:20524139 PMCid:PMC3138933
- Shabbir S, Tariq A, and Akram MU (2013). 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): 600-603. https://doi.org/10.7763/IJFCC.2013.V2.235
- Sharma N and Kothari P (2017). Study on segmentation of retinal blood vessels using wavelet filter methodology. International Journal of Innovative Research in Computer and Communication Engineering, 5(1): 1059–1065.
- Singh D and Singh B (2014). A new morphology based approach for blood vessel segmentation in retinal images. In the Annual IEEE India Conference, IEEE, Pune, India: 1-6. https://doi.org/10.1109/INDICON.2014.7030686
- Sun K, Chen Z, Jiang S, and Wang Y (2011). Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram. Journal of Medical Systems, 35(5): 811-824. https://doi.org/10.1007/s10916-010-9466-3 PMid:20703728
- Wong TY, Klein R, Sharrett AR, Schmidt MI, Pankow JS, and Couper DJ (2002). Retinal arteriolar narrowing and risk of diabetes mellitus in middle-aged persons. Jama, 287(19): 2528–2533. https://doi.org/10.1001/jama.287.19.2528 PMid:12020333
- You S, Bas E, Erdogmus D, and Kalpathy-Cramer J (2011). Principal curved based retinal vessel segmentation towards diagnosis of retinal diseases. In the First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, IEEE, San Jose, USA: 331-337. https://doi.org/10.1109/HISB.2011.39
- Zaki SKM, Zulkifley MA, and Nazari A (2014). Tracing of retinal blood vessels through edge information. In the IEEE International Conference on Control System, Computing and Engineering, IEEE, Batu Ferringhi, Malaysia: 13-17. https://doi.org/10.1109/ICCSCE.2014.7072681
- Zhao YQ, Wang XH, Wang XF, and Shih FY (2014). Retinal vessels segmentation based on level set and region growing. Pattern Recognition, 47(7): 2437-2446. https://doi.org/10.1016/j.patcog.2014.01.006