Focus Evaluation Approach for Retinal Images

Diana Veiga, Carla Pereira, Manuel Ferreira, Luís Gonçalves, João Monteiro

2014

Abstract

Digital fundus photographs are often used to provide clinical diagnostic information about several pathologies such as diabetes, glaucoma, macular degeneration and vascular and neurologic disorders. To allow a precise analysis, digital fundus image quality should be assessed to evaluate if minimum requirements are present. Focus is one of the causes of low image quality. This paper describes a method that automatically classifies fundus images as focused or defocused. Various focus measures described in literature were tested and included in a feature vector for the classification step. A neural network classifier was used. HEI-MED and MESSIDOR image sets were utilized in the training and testing phase, respectively. All images were correctly classified by the proposed algorithm.

References

  1. Giancardo, L. et al., 2012. Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical image analysis, 16(1), pp.216-26.
  2. Marrugo, A.G. et al., 2012. Anisotropy-based robust focus measure for non-mydriatic retinal imaging. Journal of biomedical optics, 17(7), p.076021.
  3. Moscaritolo, M. et al., 2009. An image based autofocusing algorithm for digital fundus photography. IEEE transactions on medical imaging, 28(11), pp.1703-7.
  4. Papakostas, G. A., Mertzios, B.G. & Karras, D. a., 2009. Performance of the Orthogonal Moments in Reconstructing Biomedical Images. 2009 16th International Conference on Systems, Signals and Image Processing, pp.1-4.
  5. Papakostas, G. & Koulouriotis, D., 2009. A General Framework for Computation of Biomedical Image Moments. Biomedical Engineering Trends in Electronics, Communications and Software.
  6. Pertuz, S., Puig, D. & Garcia, M. A., 2013. Analysis of focus measure operators for shape-from-focus. Pattern Recognition, 46(5), pp.1415-1432.
  7. Wee, C.-Y. et al., 2010. Image quality assessment by discrete orthogonal moments. Pattern Recognition, 43(12), pp.4055-4068.
  8. Yang, G. & Nelson, B., 2003. Wavelet-based autofocusing and unsupervised segmentation of microscopic images. International Conference on Intelligent Robots and Systems, Proceedings of the 2003 IEEE/RSJ, (October).
  9. Yap, P. T. & Raveendran, P., 2004. Image focus measure based on Chebyshev moments. IEEE Proc.-Vision, Image and Signal Processing, 151(2).
Download


Paper Citation


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Focus Evaluation Approach for Retinal Images
SN - 978-989-758-003-1
AU - Veiga D.
AU - Pereira C.
AU - Ferreira M.
AU - Gonçalves L.
AU - Monteiro J.
PY - 2014
SP - 456
EP - 461
DO - 10.5220/0004671104560461


in Harvard Style

Veiga D., Pereira C., Ferreira M., Gonçalves L. and Monteiro J. (2014). Focus Evaluation Approach for Retinal Images . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 456-461. DOI: 10.5220/0004671104560461


in Bibtex Style

@conference{visapp14,
author={Diana Veiga and Carla Pereira and Manuel Ferreira and Luís Gonçalves and João Monteiro},
title={Focus Evaluation Approach for Retinal Images},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={456-461},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004671104560461},
isbn={978-989-758-003-1},
}