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Towards an Automatic Clinical Classification of Age-Related Macular Degeneration

  • Thanh Vân PhanEmail author
  • Lama Seoud
  • Farida Cheriet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

Age-related macular degeneration (AMD) is the leading cause of visual deficiency and irreversible blindness for elderly individuals in Western countries. Its screening relies on human analysis of fundus images which often leads to inter- and intra-expert variability. With the aim of developing an automatic grading system for AMD, this paper focuses on identifying the best features for automatic detection of AMD in fundus images. First, different features based on local binary pattern (LBP), run-length matrix, color or gradient information are computed. Then, a feature selection is applied for dimensionality reduction. Finally, a support vector machine is trained to determine the presence or absence of AMD. Experiments were conducted on a dataset of 140 fundus images. A classification performance with an accuracy of 96 % is achieved on preprocessed images of macula area using LBP features.

Keywords

Age-related macular degeneration Fundus photography Automatic grading system Texture analysis Support vector machine 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.École Polytechnique de MontréalMontrealCanada
  2. 2.DIAGNOS Inc.BrossardCanada

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