JILSA  Vol.5 No.3 , August 2013
Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine
ABSTRACT

Support vector machine (SVM) has become an increasingly popular tool for machine learning tasks involving classification. In this paper, we present a simple and effective method of detect and classify hard exudates. Automatic detection of hard exudates from retinal images is worth-studying problem since hard exudates are associated with diabetic retinopathy and have been found to be one of the most prevalent earliest signs of retinopathy. The algorithm is based on Discrete Cosine Transform (DCT) analysis and SVM makes use of color information to perform the classification of retinal exudates. We prospectively assessed the algorithm performance using a database containing 1200 retinal images with variable color, brightness, and quality. Results of the proposed system can achieve a diagnostic accuracy with 97.0% sensitivity and 98.7% specificity for the identification of images containing any evidence of retinopathy.


Cite this paper
R. Mansour, E. Abdelrahim and A. Al-Johani, "Identification of Diabetic Retinal Exudates in Digital Color Images Using Support Vector Machine," Journal of Intelligent Learning Systems and Applications, Vol. 5 No. 3, 2013, pp. 135-142. doi: 10.4236/jilsa.2013.53015.
References
[1]   D. E. Singer, D. M. Nathan, H. A. Fogel, A. P. Schachat, “Screening for Diabetic Retinopathy,” Annals of Internal Medicine, Vol. 116, No. 8, 1992, pp. 60-71. doi:10.7326/0003-4819-116-8-660

[2]   M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp and J. V. Forrester, “The Preprocessing of Retinal Images for the Detection of Fluoresce in Leakage,” Physics in Medicine & Biology, Vol. 44, No. 1, 1999, pp. 293-308. doi:10.1088/0031-9155/44/1/021

[3]   M. J. Cree, E. Gamble and D. Cornforth, “Colour Nor malisation to Reduce Inter-Patient and Intra-Patient Vari ability in Micro Aneurysm Detection in Colour Retinal Images,” Proceedings of the APRS Workshop on Digital Image Computing, Brisbane, 21 February 2005, pp. 163-168.

[4]   M. Foracchia, M. Grisan and A. Ruggeri, “Luminosity and Contrast Normalization in Retinal Images,” Medical Image Analysis, Vol. 9, No. 3, 2003, pp. 79-90.

[5]   A. Pinz, M. Prantl and P. Datlinger, “Mapping the Human Retina,” IEEE Transactions on Medical Imaging, Vol. 17, No. 4, 1998, pp. 606-619. doi:10.1109/42.730405

[6]   N. Patton, T. M. Aslam, T. MacGillivray, I. J. Deary, B. Dhillon, R. H. Eikelboom, et al., “Retinal Image Analysis: Concepts, Applications and Potential,” Progress in Reti nal and Eye Research, Vol. 25, No. 1, 2006, pp. 99-127. doi:10.1016/j.preteyeres.2005.07.001

[7]   S. Bjorvis, M. A. Johansen and K. Fossen, “An Economic Analysis of Screening for Diabetic Retinopathy,” Journal of Telemedicine and Telecare, Vol. 8, No. 1, 2002, pp. 32-35.

[8]   S. S. Feman, T. C. Leonard-Martin and J. S. Andrews, “A Quantitative System to Evaluate Diabetic Retinopathy from Fundus Photographs,” Investigative Ophthalmology & Visual Science, Vol. 36, No. 1, 1995, pp. 174-181.

[9]   A. M. Aibinu, M. I. Iqbal, A. A. Shafie, M. J. E. Salami and M. Nilsson, “Vascular Intersection Detection in Ret ina Fundus Images Using a New Hybrid Approach,” Computers in Biology and Medicine, Vol. 40, No. 1, 2010, pp. 81-89. doi:10.1016/j.compbiomed.2009.11.004

[10]   A. M. Aibinu, M. I. Iqbal, M. Nilsson and M. J. E. Salami, “A New Method of Correcting Uneven Illumination Prob lem in Fundus Images,” International Conference on Ro botics, Vision, Information, and Signal Processing, Penang, 28-30 November 2007, pp. 445-449.

[11]   G. G. Gardner, D. Keating, T. H. Williamson and A. T. Elliot, “Automatic Detection of Diabetic Retinopathy Using an Artificial Neural Network: A Screening Tool,” British Journal of Ophthalmology, Vol. 80, No. 11, 1996, pp. 940-944. doi:10.1136/bjo.80.11.940

[12]   C. Sinthanayothin, J. F. Boyce, H. L. Cook and T. H. Williamson, “Automated Localization of the Optic Disc Fovea and Retinal Blood Vessels from Digital Colour Fundus Images,” British Journal of Ophthalmology, Vol. 83, No. 8, 1999, pp. 231-238. doi:10.1136/bjo.83.8.902

[13]   H. Wang, W. Hsu, K. G. Goh and M. L. Lee, “An Effect tive Approach to Detect Lesions in Color Retinal Im ages,” Proceedings of IEEE Conference on Computer Vi sion and Pattern Recognition, Hilton Head Island, 13-15 June 2000, pp. 181-186.

[14]   H. Li and O. Chutatape, “A Model-Based Approach for Automated Feature Extraction in Fundus Images,” Inter national Conference on Computer Vision (ICCV), Nice, 13-16 October 2003, pp. 394-399.

[15]   D. Usher, M. Dumskyj, M. Himaga, T. H. Williamson, S. Nussey and J. Boyce, “Automated Detection of Diabetic Retinopathy in Digital Retinal Images: A Tool for Dia betic Retinopathy Screening,” Diabetic Medicine, Vol. 21, No. 1, 2004, pp. 84-90. doi:10.1046/j.1464-5491.2003.01085.x

[16]   K. G. Goh, W. Hsu, L. Lee and H. Wang, “ADRIS: An Automatic Diabetic Retinal Image Screening System,” In: J. C. Krzysztof, Ed., Medical Data Mining and Knowl edge Discovery, Physica-Verlag, Heidelberg, 2001, pp. 181-210.

[17]   B. M. Ege, O. K. Hejlese, O. V. Larsen, K. Moller, B. Jennings, D. Kerr and D. A. Cavan, “Screening for Dia betic Retinopathy Using Computer Based Image Analysis and Statistical Classification,” Computer Methods and Programs in Biomedicine, Vol. 62, No. 3, 2000, pp. 165-175. doi:10.1016/S0169-2607(00)00065-1

[18]   A. Osareh, M. Mirmehdi, B. Thomas and R. Markham, “Comparative Exudate Classification Using Support Ve ctor Machines and Neural Networks,” In: T. Dohi and R. Kikinis, Eds., Medical Image Computing and Com puter-Assisted Intervention, Springer, Berlin, 2002, pp. 413-420.

[19]   L. Tang, M. Niemeijer and M. D. Abrilmoff, “Splat Feature Classification: Detection of the Presence of Large Retinal Hemorrhages,” IEEE International Symposium on Biomedical Imaging, Chicago, 30 March 2011-2 April 2011, pp. 681-684.

[20]   J. J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Vier gever and B. van Ginneken, “Ridge Based Vessel Segmentation in Color Images of the Retina,” IEEE Transactions on Medical Imaging, Vol. 23, No. 4, 2004, pp. 501-509. doi:10.1109/TMI.2004.825627

[21]   Messidor, “Methods to Evaluate Segmentation and In dexing Techniques in the Field of Retinal Ophthalmology Techno-Vision Project”. http://messidor.crihan.fr/

[22]   X. Zhang and O. Chutatape, “Top-Down and Bottom-Up Strategies in Lesion Detection of Background Diabetic Retinopathy,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2, 20-25 June 2005, pp. 422-428.

[23]   R. F. Mansour, “Using Genetic Algorithm for Identifica tion of Diabetic Retinal Exudates in Digital Color Im ages,” Journal of Intelligent Learning Systems and Ap plications, Vol. 4, No. 3, 2012, pp. 188-198. doi:10.4236/jilsa.2012.43019

[24]   M. Sabaghi, S. R. Hadianamrei, M. Fattahi, M. R. Kou chaki and A. Zahedi, “Retinal Identification System Ba sed on the Combination of Fourier and Wavelet Transform,” Journal of Signal and Information Processing, Vol. 3, No. 1, 2012, pp. 35-38. doi:10.4236/jsip.2012.31005

[25]   R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” Pearson Education Inc., New Delhi, 2003.

[26]   M. Kirby and L. Sirovich, “Application of the Karhunen Loeve Procedure for the Characterization of Human Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. 1, 1990, pp. 103-108. doi:10.4236/jsip.2012.31005

[27]   O. Chapelle, P. Haffner and V. N. Vapnik, “SVMs for Histogram-Based Image Classification,” IEEE Transac tions on Neural Networks, Vol. l0, No. 5, 1999, pp. 1055-1064.

[28]   H. Bhavsar and M. H. Panchal, “A Review on Support Vector Machine for Data Classification,” International Journal of Advanced Research in Computer Engineering & Technology, Vol. 1, No. 10, 2012.

[29]   K. Wisaeng, N. Hiransakolwongi and E. Pothiruk, “Automatic Detection of Retinal Exudates Using a Sup port Vector Machine,” Applied Medical Informatics, Vol. 32, No. 1, 2013, pp. 33-42.

[30]   The STARE Database, 2009. http://www.ces.clemson.edu/ahoover/stare

[31]   T. Fawcett, “ROC Graphs: Notes and Practical Consid erations for Researchers,” Technical Report MS1143 Ex tended Version of HPL-2003-4, HP Laboratories, 2004.

[32]   M. Kallergi, “Evaluation Strategies for Medical-Image Analysis and Processing Methodologies,” In: L. Costari dou, Ed., Medical Image Analysis Methods: The Electri cal Engineering and Applied Signal Processing Series, CRC Press, Boca, 2005, pp. 433-471,

 
 
Top