Segmentation of exudates to assess diabetic retinopathy by reni’s entropy based thresholding

  • Abstract
  • Keywords
  • References
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  • Abstract

    Objective: Diabetic retinopathy is a critical pathological disease condition which affects the lives of millions of people everyday. Exudates found in the eye are one of the important signs of Diabetic retinopathy. This work aims to segment exudates for faster detection and treatment of Diabetic retinopathy.Methods: This paper proposes a robust and efficient method to segment exu-dates. Initial pre-processing work applies adaptive unsharp masking which sharps the areas based on the level of smoothness in the image preventing accentuation of noise. Optic disc is removed by active contour model. The exudates are then segmented by Renyi’s Entropy based thresholding which choses the optimal threshold for segmentation, exploiting Renyi’s entropy da-ta.Results: The performance of the proposed system was evaluated and found better than state of art results giving accuracy, sensitivity and specificity 94.5%, 95.1% and 96.2% respectively.Conclusion: Effective computer aided system is essential for accurate exudates detection. The proposed algorithm utilises the advantages of adaptive unsharp masking in medical image pro-cessing along with Renyi’s entropy based thresholding to detect Exudates, which performs better than traditional thresholding techniques.



  • Keywords

    Lung; Feature Extraction; Detection; Pulmonary Embolism

  • References

      [1] L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, S. Garg, K.W.Tobinand E. Chaum, ‘Exudates based Detection In Fundus Images Using Publicly Available Datasets’, Med Image Ana., vol 16,no.1,pg 216-226,2012..

      [2] C. Agurto, V. Murray, H. Yu, J.Wigdahl, M.Pattichis, S.Nemeth et al., ‘A Multiscale Optimization Approach To Detect Exudates In The Macula’, IEEE Journal of Biomedical and Health Informatics, vol.18,no.4,pg.13228-1336,2014.

      [3] A. F. Aqeel and S. Ganesan, ‘Automated Algorithm For Retinal Images And Drusens Detection , ‘Segmentation And Measurement’ , In Proceedings Of The IEEE International Conference On Electro Information Technology, pg 206-215, 2014.

      [4] P.M Rokade and R. R. Manza, ‘Automatic Detection Of Hard Exudates In Retinal Images Using Haar WAVELET Transform,’ International Journal Of Application Or Innovation In Engineering &Management, Vol .4,No.5, pg 402-410,2015.

      [5] H. Li, O. Chutatape, ‘Automated Feature Extraction in Color Fundus Retinal Images by a Model Based Approach IEEE Transactions on Biomedical Engineering. Vol 51, No.2, pg 264-254, 2004.

      [6] B.M. Ege, L. Hejlese, O. V. Larsen, B. Moller, M. Kerr, ‘Screening for Diabetic Retinopathy Using Computer Based Image Analysis And Statistical Classification’, Computer Methods Programs Biomed, Vol 63, no.3,pg 165-175.2000.

      [7] D. Kavitha and S.S. Devi, ‘Automatic Detection Of Optic Disc And Exudates In Retinal Images’, IEEE International Conference On Intelligent Sensing and Information Processing, pg.501-506,2005.

      [8] D. Usher, M. Dumskyj, M Himaga, T. H. Williamson, S. Nussey, J. Boyce, ‘Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Diabetic Retinopathy Screening.’ Diabetes 84-90.2004.

      [9] A. Somasundaram and J. Prabhu, ‘Detection of Exudates for the Diagnosis of Diabetic Retinopathy’, International Journal of Innovation and Applied Studies, Vol.3, No.1, pg 116-120, 2013.

      [10] A. Osareh, M. Mirmehdi, B. Thomas, R. Markham, ‘Automated Identification Of Diabetic Retinal Exudates In Retinal Exudates In Digital Color Images, Br.J. Ophthamol.,Pg 1220-1223,2003.

      [11] Dunn, J.C, ‘A Fuzzy Relative Of The ISODATA Process And Its Use In Detecting Compact Well Separated Clusters,Cybernetics And Systemspg 32-57,1973.

      [12] A. Sopharak, B. Uyyanonvarab, S. Barman, T. H. Williamson. ‘Automatic Detection Of Diabetic Retinopathy Exudates From Non – Dilated Morphology Methods, Computerized Medical Imaging And Graphics, Pg 720-727, 2008.

      [13] C .E .Hann, M. Narbot, M.Macaskill, ‘Diabetic Retinopathy Detection Using Geometrical Techniques Related to the Underlying Pathology. Int. Conf. Image Vis.Comput,pg1-8,2010.

      [14] R. Annunziata, A. Garzelli, L. Ballerini, A. Mecocci And E. Trucco, ‘Leveraging Multiscale Hessian-Based Enhancement With A Novel Exudate Inpainting Technique For Retinal Vessel Segmentation, ‘ IEEE Journal Of Biomedical And Health Informatics,Vol.20,No.4,Pg 1129-1138,2016.

      [15] TomiKauppi, Kalensnykiene V, Kamarainen J-K, Lensu L, Sorri, I, Raninen A, R. Voutilanien, Uusitalo, H.Kalviainen,J. Pietila, DIARETDB1 Diabetic Retinopathy Database And Evaluation Protocol,Online: http://

      [16] Andrea Poesel, Giovanni Ramponi and V.John Mathews, ‘Image Enhancement Via Adaptive Unsharp Masking’, IEEE Transactions on Image Processing, Vol 9, No.3, Pg 505-510,200 0.

      [17] S.Weeratunga and C. Kamath, ‘ An Investigation Of Implicit Active Contours For Scientific Image Segmentation’ ,In Proceedings of SPIE, vol 5308,pg 210-221,2004.

      [18] J. Lin, ‘Divergence Measures Based on the Shannon Entropy’, IEEE Trans. Inf Theory, Vol.37 (1), pg 145-151, 1991.

      [19] Prasanna Sahoo, Carrye Wilkins and Jerry Yeager, Threshold Selection Using Renyi’s Entropy Thresholding’, Pattern Recognition, Elsevier, Vol.30, No.1, pg 71-84, 1997.

      [20] P.K.S and A.C.K.W.J.N .Kapur, ‘ A New Method For Gray Level Picture Thresholding Using The Entropy Of The Histogram,Computer Vision Graphic,Image Process,Vol.29,Pg 273-285,1985.




Article ID: 16570
DOI: 10.14419/ijet.v7i2.25.16570

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