April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Integration of multiple source data for glaucoma detection
Author Affiliations & Notes
  • Jimmy Jiang Liu
    Ocular Imaging, Inst for Infocomm Research, A*STAR, Singapore, Singapore
  • Yanwu Xu
    Ocular Imaging, Inst for Infocomm Research, A*STAR, Singapore, Singapore
  • Jun Cheng
    Ocular Imaging, Inst for Infocomm Research, A*STAR, Singapore, Singapore
  • Zhang Zhuo
    Ocular Imaging, Inst for Infocomm Research, A*STAR, Singapore, Singapore
  • Damon W K Wong
    Ocular Imaging, Inst for Infocomm Research, A*STAR, Singapore, Singapore
  • Fengshou Yin
    Ocular Imaging, Inst for Infocomm Research, A*STAR, Singapore, Singapore
  • Tien Y Wong
    Singapore Eye Research Instittute, Singapore, Singapore
  • Footnotes
    Commercial Relationships Jimmy Jiang Liu, None; Yanwu Xu, None; Jun Cheng, None; Zhang Zhuo, None; Damon Wong, None; Fengshou Yin, None; Tien Wong, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4820. doi:
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    • Get Citation

      Jimmy Jiang Liu, Yanwu Xu, Jun Cheng, Zhang Zhuo, Damon W K Wong, Fengshou Yin, Tien Y Wong; Integration of multiple source data for glaucoma detection. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4820.

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

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Abstract
 
Purpose
 

To introduce an approach for the detection of glaucoma through the integration of data from multiple sources and to evaluate the performance of this approach.

 
Methods
 

We developed a multiple source fusion approach for the detection of glaucoma. This approach integrates information from demographic data, imaging information from retinal image features, and genetic information from genome single-nucleotide polymorphisms (SNPs) through the use of a multiple kernel learning framework. In multiple kernel learning, a combined kernel is formed from a weighted linear combination of base kernels instead of a single kernel. This framework is used to fuse the heterogenous data sources for the multi-source glaucoma detection approach. The framework is shown in Fig. 1. The area under the Receiver Operating Characteristic Curve (AUC) for glaucoma detection is used to evaluate our multi-source approach. As a comparison we have also included the detection performance from each of the individual data sources.

 
Results
 

The approach was tested on a dataset from 2,258 individuals from the Singapore Malay Eye Study which includes patient demographic data, retinal images and SNP information. Glaucoma was diagnosed in 100 of the individuals from the dataset. The results show that the combination of all three sources achieves an AUC of 0.869, which is better than the result from any other individual data source or combination of any two sources.

 
Conclusions
 

An approach for the fusion of multiple data sources from demographic, imaging and genetic data for glaucoma detection is tested. The promising experimental results on a large dataset show the potential of multi-source integration for disease detection.

 
 
Fig. 1: Flowchart of the multi-source integration approach for glaucoma detection
 
Fig. 1: Flowchart of the multi-source integration approach for glaucoma detection
 
Keywords: 549 image processing  
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