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Texture based prelens tear film segmentation in interferometry images

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Abstract

Interferometric imaging has been identified as a novel approach to the evaluation of prelens tear film (PLTF) thickness in contact lens patients. In this paper, we present a texture based segmentation approach for the detection of tear film breakup regions on interferometry images. First, the textural information was extracted from the studied images using a bank of Gabor filters. A novel classifier, EM-MDA, which integrates traditional Expectation-Maximization with Multiple Discriminant Analysis, was then trained for the recognition of breakup regions of the PLTF. Experimental results provided a correct classification rate of 91.0% which proved significantly higher compared to traditional EM or well known Linear Discriminant Analysis.

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Authors and Affiliations

  1. Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Dijia Wu & Kim L. Boyer

  2. College of Optometry, The Ohio State University, Columbus, OH, 43210, USA

    Jason J. Nichols & Peter E. King-Smith

Authors
  1. Dijia Wu
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  2. Kim L. Boyer
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  3. Jason J. Nichols
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  4. Peter E. King-Smith
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Correspondence to Dijia Wu.

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Wu, D., Boyer, K.L., Nichols, J.J. et al. Texture based prelens tear film segmentation in interferometry images. Machine Vision and Applications 21, 253–259 (2010). https://doi.org/10.1007/s00138-008-0155-x

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  • DOI: https://doi.org/10.1007/s00138-008-0155-x

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