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.

  • Abstract

    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.

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  • 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

    Received date: 2017-12-29

    Accepted date: 2018-01-23

    Published date: 2018-01-30