Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images

  • Authors

    • Sheela N. Sri Jayachamarajendra College of Engineering, Mysuru, Karnataka, India.
    • Basavaraj L. ATME College of Engineering, Mysuru, Karnataka, India
    2018-01-30
    https://doi.org/10.14419/ijet.v7i1.8969
  • Drusen, Gabor Filters, Genetic Algorithm, Principal Component Analysis, Support Vector Machine.
  • Human eye can be affected by different types of diseases. Age-Related Macular Degeneration (AMD) is one of the such diseases, and it mainly occurs after 50 years of age. This disease is characterized by the occurrence of yellow spots called as Drusen. In this work, an automated method for the detection of drusen in Fundus image has been developed, and it has been tested on 70 images consisting of 30 normal images and 40 images with drusen. Performance of the Support Vector Machine (SVM) and K Nearest Neighbor (KNN) classifier has been evaluated using Data's reduction using Principle Component Analysis (PCA) and Data's selection using Genetic Algorithm (GA).Performance evaluation has been done in terms of accuracy, sensitivity, specificity, misclassification rate, positive predictive rate, negative predictive rate and Youden’s Index. The proposed method has achieved highest accuracy of 98.7% when data selection using Genetic Algorithm has been applied.

  • References

    1. [1] Rama D. Jager, M.D., William F. Mieler, M.D., and Joan W. Miller, M.D., “Age Related Macular Degeneration: Review Articleâ€, the New England Journal of Medicine. 2008; 358:2606-17.

      [2] Catherine Bowes Rickman, SinaFarsiu, Cynthia A. Toth, and Mikael Klingeborn, “Dry Age-Related Macular Degeneration: Mechanisms, Therapeutic Targets, and Imagingâ€. Investigative Ophthalmology & Visual Science, Vol.54, (2013), pp. ORSF68-ORSF80. https://doi.org/10.1167/iovs.13-12757.

      [3] Wan Ling Wong, Xinyi Su, Xiang Li, Chui Ming G Cheung, Ronald Klein, Ching-Yu Cheng, Tien Yin Wong, “Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysisâ€, The Lancet Global Health, Vol 2, No. 2, (2014), pp. e106-e116, https://doi.org/10.1016/S2214-109X(13)70145-1.

      [4] Mark J. J. P. van Grinsven, Yara T. E. Lechanteur, Johannes P. H. van de Ven, Bram van Ginneken, Carel B. Hoyng, Thomas Theelen, and Clara. S´anchez, “Automatic Drusen Quantification and Risk Assessment of Age-Related Macular Degeneration on Color Fundus Imagesâ€, Investigative Ophthalmology & Visual Science,Vol.54, (2013), pp. 3019-3027. https://doi.org/10.1167/iovs.12-11449.

      [5] AlauddinBhuiyan, Ryo Kawasaki, Mariko Sasaki, EcosseLamoureux, KotagiriRamamohanarao, Robyn Guymer, Tien Y Wong and KanagasingamYogesan, “Drusen Detection and Quantification for Early Identification of Age Related Macular Degeneration using Color Fundus Imagingâ€, Journal of Clinical and Experimental Ophthalmology, Vol. 4, No. 5, (2013), https://doi.org/10.4172/2155-9570.1000305.

      [6] Mora A., Vieira P., Fonseca J., “Advances in Image Processing Techniques for DrusensDetection and Quantification in Fundus Imagesâ€. Emerging Trends in Technological Innovation. DoCEIS, IFIP Advances in Information and Communication Technology, vol 314. Springer. (2010). https://doi.org/10.1007/978-3-642-11628-5_32.

      [7] Rama Prasath.A, M.M.Ramya, “ Automated Drusen Grading System In Fundus Image Using Fuzzy C-Means Clusteringâ€, International Journal of Engineering Technology, Vol 6, No 2, (2014).

      [8] Thanh Vân Phan, Lama Seoud, HadiChakor, and Farida Cheriet, “Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Imagesâ€, Journal of Ophthalmology, Volume 2016, Article ID 5893601, 11 pages, Hindawi Publishing Corporation, https://doi.org/10.1155/2016/5893601.

      [9] Ziyang Liang, Damon W.K. Wong, Jiang Liu, KapLuk Chan, Tien Yin Wong, “Towards automatic detection of age-related macular degeneration in retinal fundus imagesâ€, 32nd Annual International Conference of the IEEE EMBS, Buenos Aires, Argentina, Aug 31 - Sep 4, 2010.

      [10] P. Burlina, D.E. Freund, B. Dupas, and N. Bressler, “Automatic Screening of Age-Related Macular Degeneration and Retinal Abnormalitiesâ€, 33rd Annual International Conference of the IEEE EMBS Boston, Massachusetts USA, Aug 30 - Sep 3, 2011. https://doi.org/10.1109/IEMBS.2011.6090984.

      [11] Xiangang Cheng, Damon Wing Kee Wong, Jiang Liu, Beng-Hai Lee, Ngan Meng Tan, Jielin Zhang, Ching Yu Cheng, Gemmy Cheung and Tien Yin Wong, “Automatic localization of retinal landmarks†34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 Aug - 1 Sept, 2012.

      [12] Muthu Rama Krishnan Mookiah, U. Rajendra Acharya, Joel E.W. Koh, Chua Kuang Chua, Jen Hong Tan, Vinod Chandran, Choo Min Lim, Kevin Noronha, Augustinus Laude, Louis Tong, “Decision support system for age‑related macular degeneration using discrete wavelet transformâ€, Medical & Biological Engineering & Computing, Vol 52, No. 9, (2014), pp 781–796, https://doi.org/10.1007/s11517-014-1180-8.

      [13] Muthu Rama Krishnan Mookiah, U Rajendra Acharya, Joel E. W. Koh, Vinod Chandran, Chua Kuang Chua, Jen Hong Tan, Choo Min Lim, E. Y. K Ng, Kevin Noronha, Louis Tong, Augustinus Laude. “Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus imagesâ€, Computers in Biology and Medicine, Volume 53, 2014, pp 55-64, https://doi.org/10.1016/j.compbiomed.2014.07.015.

      [14] Saima Waseem, M. Usman Akram, Bilal Ashfaq Ahmed. “Drusen Detection From Colored Fundus Images for Diagnosis of Age Related Macular Degenerationâ€, 7th IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 2014. https://doi.org/10.1109/ICIAFS.2014.7069581.

      [15] John G.Daugman, Two Dimensional Spectral Analysis of Cortical Receptive Field Profiles, Vision Group, Vol. 20, No. 10, (1980), pp 847-856, https://doi.org/10.1016/0042-6989(80)90065-6.

      [16] Robert M. Haralick, K. Shanmugam, Its’hakDinstein. “Textural Features for Image Classificationâ€, IEEE Transactions on Systems, Man and Cybernetics, Vol SMC -3, No. 6, (1973), pp. 610-621.

      [17] Dibyadeep Nandi; Amira S. Ashour; SouravSamanta; Sayan Chakraborty; Mohammed A.M. Salem; NilanjanDey “Principal Component Analysis in Medical Image Processing: A Studyâ€, International Journal of Image Mining, Vol 1, No. 1, (2015), pp 65 – 86, https://doi.org/10.1504/IJIM.2015.070024.

      [18] J. H. Holland, “Adaptation in Natural and Artificial Systemsâ€, The University of Michigan Press, Ann Arbor, Michigan, USA, 1975

      [19] Corinna Cortes, Vladimir Vapnik. “Support-Vector Networksâ€, Machine Learning, Vol. 20, No 3, (1995), pp. 273-297. https://doi.org/10.1007/BF00994018.

      [20] D.T.Larose, “Discovering Knowledge in Data; An Introduction to Data Miningâ€, Wiley Interscience, (2004), pp. 90-109.https://doi.org/10.1002/0471687545.

      [21] Damon W.K. Wong, Jiang Liu, Xiangang Cheng, Jielin Zhang, Fengshou Yin, Mayuri Bhargava, Gemmy C.M. Cheung, Tien Yin Wong, “THALIA - An automatic hierarchical analysis system to detect drusen lesion images for amd assessmentâ€, IEEE 10th International Symposium onBiomedical Imaging (ISBI), (2013), https://doi.org/10.1109/ISBI.2013.6556617.

  • Downloads

  • How to Cite

    N., S., & L., B. (2018). Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images. International Journal of Engineering & Technology, 7(1), 115-120. https://doi.org/10.14419/ijet.v7i1.8969