An automated exudates detection in diabetic retinopathy fundus images using multi kernel spatial fuzzy c means clustering method

  • Authors

    • R Ravindraiah
    • S Chandra Mohan Reddy
    2018-02-09
    https://doi.org/10.14419/ijet.v7i1.8.9442
  • Exudates, Diabetic Retinopathy (DR), Fuzzy C Means Clustering algorithm (FCM), Multiple kernel Spatial FCM (MKSFCM).

  • Microvasculature change associated with tenacious Hyperglycemia are the hostile effects accompanying to diabetes mellitus. Diabetic Retinopathy (DR) is a progressive complication, which leads to retinal permeability, ischemia, neovascularization and macular edema. The pathology is characterized by variation in capillary diameter, size of microaneurysm, hemorrhage exudates. Thus it stimulates the growth of new abnormal blood vessels so as to nourish the eye muscles. But these newly grown blood vessels are subtle, and may get burst. Therefore it leads to leakage of blood, protein based particles named as exudates. Early determination of the DR signs will help the diabetic patient to eradicate austere vision damage. Medical image processing methods helps the ophthalmologists in easy diagnosis, and to estimate the severity of the pathology. Fuzzy based clustering methods are simple and effective methods that will classify the pathos. This work furnish an improved fuzzy clustering method with induced multi kernel and spatial constraint. Statistical evaluation is done to evaluate the performance of the proposed method.

  • References

    1. [1] “Diabetic retinopathy: what you should knowâ€, National Eye Institute, National Institute of Health, NIH Publication No. 15-2171, 2017.

      [2] Dharmalingam M. (2003) “Diabetic retinopathy–risk factors and strategies in preventionâ€, Laser, vol. 51, pp. 77.

      [3] Stratton I. M., Aldington S. J., Taylor D. J. et al. (2012) “A Simple Risk Stratification for Time to Development of Sight-Threatening Diabetic Retinopathy. Diabetes Careâ€, pp. 1-6, November 12.

      [4] Neeti Gupta, Rohit Gupta (Jan. - Mar. 2015), “Diabetic Retinopathy – An Updateâ€, JIMSA Vol. 28 No. 1.

      [5] Akara Sopharak, Bunyarit Uyyanonvara, Sarah Barman, Thomas H. Williamson (2008),†Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methodsâ€, Computerized Medical Imaging and Graphics 32, pp 720–727. https://doi.org/10.1016/j.compmedimag.2008.08.009.

      [6] C.I. Sanchez, R. Hornero, M.I. Lopez, J. Poza. 2004 “Retinal Image Analysis to Detect and Quantify Lesions Associated with Diabetic Retinopathyâ€, Proc. 26th IEEE Annual International Conf. on Engineering in Medicine and Biology Society (EMBC), 3, 1624 – 1627. https://doi.org/10.1109/IEMBS.2004.1403492.

      [7] Selvathi D, N.B.Prakash, and Neethi Balagopal. 2012 “Automated Detection of Diabetic Retinopathy for Early Diagnosis using Feature Extraction and Support Vector Machineâ€, International Journal of Emerging Technology and Advanced Engineering (ISSN 2250-2459, Volume 2, Issue 11.

      [8] Jayaraman S, Esakkirajan S. and Veera Kumar T. (2013), “Digital Image processingâ€, McGraw Hill Education (India) pvt ltd.

      [9] Sohini Roychowdhury, Dara D. Koozekanani, and Keshab K. Parhi (September-2014), “DREAM: Diabetic Retinopathy Analysis Using Machine Learningâ€, IEEE Journal of Biomedical and Health Information, VOL. 18 pp. 1717 - 1728, No. 5.

      [10] Mahendran Gandhi, and Dr. R. Dhanasekaran (2013), “Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifierâ€, Communications and Signal Processing (lCCSP), International Conference on pp. 873 – 877.

      [11] Anushikha Singh, Namita Sengar, Malay Kishore Dutta, Kamil Riha, Jiri Minar (2015), “Automatic exudates detection in fundus image using intensity thresholding and morphologyâ€, Seventh ICUMT, IEEE, p. 330 – 334. https://doi.org/10.1109/ICUMT.2015.7382452.

      [12] Mahendran Gandhi, and Dr. R. Dhanasekaran (April 2013), “Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM Classifierâ€. International conference on Communication and Signal Processing, Melmaruvathur, India, pp.952-956. https://doi.org/10.1109/iccsp.2013.6577181.

      [13] Xiwei Zhang, Guillaume Thibault, Etienne Decencière, Beatriz Marcotegui (2014), “Exudate Detection in Color Retinal Images for Mass Screening of Diabetic Retinopathyâ€, Medical Image Analysis, Volume 18(7), P. 1026–1043, ELSEVIER. https://doi.org/10.1016/j.media.2014.05.004.

      [14] Dr.R.Geetha Ramanil et al (Dec. 2012), “Data Mining Method of Evaluating Classifier Prediction Accuracy in Retinal Dataâ€, Computational Intelligence & Computing Research (lCCIC), 2012 IEEE International Conference on 18-20 pp.1 - 4.

      [15] Jagadish Nayak et.al. (April 2008), “Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Imagesâ€, Journal of Medical Systems, Volume 32, Issue 2, pp 107-115. https://doi.org/10.1007/s10916-007-9113-9.

      [16] Luca Giancardo, Fabrice Meriaudeau, Thomas P. Karnowski, et.al. (January 2012), “Exudate-based diabetic macular edema detection in fundus images using publicly available datasetsâ€, Medical Image Analysis: Volume 16, Issue I, Pages 216-226. https://doi.org/10.1016/j.media.2011.07.004.

      [17] Agurto .C, et.al. (2010 Feb), “Multiscale AM-FM methods for diabetic retinopathy lesion detectionâ€, IEEE Trans Med Imaging.; 29(2):502-12. doi: 10.1 109/TM1.2009.2037 146.

      [18] Agurto.C, et.al. (Feb. 2010), “Multiscale am-fin methods for diabetic retinopathy lesion detection", Medical Imaging. IEEE Transactions, vol. 29, No. 2, pp. 502 -512. https://doi.org/10.1109/TMI.2009.2037146.

      [19] Osareh A, Mirmehdi M, Thomas B, Markham R (2001), “Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networksâ€, Medical Image Understanding Analysis, BMVA Press, UK, pp. 49-52.

      [20] Osareh, B. Shadgar, and R. Markham (2009), “A Computational- Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Imagesâ€, Information Technology in Biomedicine, IEEE Transactions on, vol. 13, pp. 535-545. https://doi.org/10.1109/TITB.2008.2007493.

      [21] Dunn J C, J.Cybernet (March, 1973), “A fuzzy relative of the ISODTATA process and its use in detecting compact well separated clustersâ€, pp 32-57.

      [22] Bezdek J C (1981), “Pattern Recognition with Fuzzy Objective Function Algorithmsâ€, New York: Plenum Press. https://doi.org/10.1007/978-1-4757-0450-1.

      [23] Long Chen, C L Philip Chen, Mingzhu Lu .A (2011), “Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentationâ€. IEEE Systems, Man, and Cybernetics Society, p. 1263 – 1274. https://doi.org/10.1109/TSMCB.2011.2124455.

      [24] Abbas Biniaz, Ataollah Abbassi, Mousa Shamsi, Afshin Ebrahimi. (2012), “Fuzzy c-means clustering based on Gaussian spatial information for brain MR image segmentationâ€, IEEE 19th Iranian Conference of on Biomedical Engineering (ICBME), p.154 – 158. https://doi.org/10.1109/ICBME.2012.6519676.

      [25] Giri Babu Kande, P.Venkata Subbaiah, T.Satya Savithri 3(2008), “Segmentation of Exudates and Optic Disc in Retinal Imagesâ€. Sixth Indian Conference on Computer Vision, Graphics & Image Processing,

      [26] Akara Sopharak , Bunyarit Uyyanonvara and Sarah Barman (2009), “Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clusteringâ€, 9, 2148-2161; https://doi.org/10.3390/s90302148.

      [27] Daniel Welfer, Jacob Scharcanski Diane Ruschel Marinho (2010), “A coarse-to-fine strategy for automatically detecting exudates in color eye fundus imagesâ€, Computerized Medical Imaging and Graphics 34, pp 228–235 https://doi.org/10.1016/j.compmedimag.2009.10.001.

      [28] Kauppi. T, Kalesnykiene T, Kamarainen J.K, Lensu L, Sorri I, Uusitalo H. Kalviainen H and Pietila. J (2006) “Diaretdb0: Evaluation database and methodology for diabetic retinopathy algorithmsâ€, Technical report Lappeenranta University of Technology Finland.

      [29] Kauppi, T., Kalesnykiene, V., Kamarainen, J.K., Lensu, L., Sorri, I., Raninen A., Voutilainen R., Uusitalo, H., Kälviäinen, H., Pietilä, J., “DIARETDB1 diabetic retinopathy database and evaluation protocolâ€, Proc of the 11th Conf. on Medical Image Understanding and Analysis (Aberystwyth, Wales, 2007). https://doi.org/10.5244/C.21.15.

      [30] ASRS: Advocating for You and Your Patients in 2017, Retina TimesSpring 2017, Vol. 35, No. 1, Issue 68.

      [31] Decencière E, et al. TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM, (2013), https://doi.org/10.1016/j.irbm.2013.01.010.

      [32] Budai, Attila; Bock, Rüdiger; Maier, Andreas; Hornegger, Joachim; Michelson, Georg (2013), “Robust Vessel Segmentation in Fundus Images, International Journal of Biomedical Imagingâ€, vol. 2013. https://doi.org/10.1155/2013/154860.

  • Downloads

  • How to Cite

    Ravindraiah, R., & Chandra Mohan Reddy, S. (2018). An automated exudates detection in diabetic retinopathy fundus images using multi kernel spatial fuzzy c means clustering method. International Journal of Engineering & Technology, 7(1.8), 10-14. https://doi.org/10.14419/ijet.v7i1.8.9442