May 2008
Volume 49, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2008
Support Vector Machine (SVM)-Based Classification of Corneal Topography
Author Affiliations & Notes
  • H. Bagherinia
    Carl Zeiss Meditec Inc., Dublin, California
  • X. Chen
    Carl Zeiss Meditec Inc., Dublin, California
  • C. Flachenecker
    Carl Zeiss Meditec Inc., Dublin, California
  • R. Angeles
    Carl Zeiss Meditec Inc., Dublin, California
  • D. Burger
    Kaiser Permanente, Oakland, California
  • P. Caroline
    Pacific University, Forest Grove, Oregon
  • J. Dishler
    Dishler Laser Institute, Denver, Colorado
  • D. Tanzer
    Naval Medical Center, San Diego, California
  • D. Schanzlin
    Ophthalmology, University of California San Diego, La Jolla, California
  • K. Reeder
    Carmel Mountain Vision Care, San Diego, California
  • Footnotes
    Commercial Relationships  H. Bagherinia, Carl Zeiss Meditec Inc., E; X. Chen, Carl Zeiss Meditec Inc., E; C. Flachenecker, Carl Zeiss Meditec Inc., E; R. Angeles, Carl Zeiss Meditec Inc., E; D. Burger, None; P. Caroline, None; J. Dishler, None; D. Tanzer, None; D. Schanzlin, None; K. Reeder, None.
  • Footnotes
    Support  None.
Investigative Ophthalmology & Visual Science May 2008, Vol.49, 1023. doi:https://doi.org/
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      H. Bagherinia, X. Chen, C. Flachenecker, R. Angeles, D. Burger, P. Caroline, J. Dishler, D. Tanzer, D. Schanzlin, K. Reeder; Support Vector Machine (SVM)-Based Classification of Corneal Topography. Invest. Ophthalmol. Vis. Sci. 2008;49(13):1023. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: : To demonstrate the feasibility of a SVM-based learning algorithm to discriminate normal corneas from other corneal conditions using topography exams collected by the ATLAS® Model 9000 Corneal Topographer (Carl Zeiss Meditec, Dublin, CA).

Methods: : A Support Vector Machine (SVM) based classifier was developed to discriminate normal corneas from other corneal conditions (such as suspect keratoconus, keratoconus, orthokeratology, pellucid marginal degeneration, myopic and hyperopic laser vision correction). The feature vector consists of 12 parameters: CIM, Shape Factor, TKM, Convexity, Centroid X, Centroid Y, Max Mean Power, Max Mean Power X, Max Mean Power Y, Mean I-S, Mean IN-ST, and Mean IT-SN. The first 3 parameters were used in the original PathFinderTM Corneal Analysis program, while the remaining 9 parameters are derived from the Mean Curvature Map. Data from the eyes of 85 normal subjects, and 239 other corneal condition subjects were used to train and evaluate the current version of the algorithm. The SVM classifier was trained using 80% of the dataset determined at random, and evaluated using the remaining 20%. The current algorithm version was used to maximize the separation between these two groups in this 12 dimensional feature space.

Results: : Early verification results based on the training data set using the current version of the algorithm show that the SVM classifier was able to discriminate normal corneas from the other corneal conditions with ≥ 90% sensitivity, specificity, and accuracy. A performance validation study is currently ongoing using a different data set. The results from this study will be presented.

Conclusions: : The use of a SVM classifier with parameters derived from the mean curvature map may become a useful tool to discriminate normal corneas from other corneal conditions based on early verification results using a randomly selected subset of the training data set.

Clinical Trial: : www.clinicaltrials.gov NCT00396188

Keywords: topography • discrimination • shape, form, contour, object perception 
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