Optic Disc Detection by Earth Mover's Distance Template Matching
This paper presents a method for the detection of OD in the retina which takes advantage of the powerful preprocessing techniques such as the contrast enhancement, Gabor wavelet transform for vessel segmentation, mathematical morphology and Earth Mover-s distance (EMD) as the matching process. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Vessel segmentation method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel-s feature vector. Feature vectors are composed of the pixel-s intensity and 2D Gabor wavelet transform responses taken at multiple scales. A simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity using the EMD. The minimum distance provides an estimate of the OD center coordinates. The method-s performance is evaluated on publicly available DRIVE and STARE databases. On the DRIVE database the OD center was detected correctly in all of the 40 images (100%) and on the STARE database the OD was detected correctly in 76 out of the 81 images, even in rather difficult pathological situations.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077002Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1862
 "Algorithms for digital image processing in diabetic retinopathy," Computerized Medical Imaging and Graphics, vol. 33, no. 8, pp. 608-622,2019.
 M. Lalonde, M. Beaulieu, and L. Gagnon, "Fast and robust optic disk detection using pyramidal decomposition and hausdorff-based template matching," IEEE Transactions on Medical Imaging, vol. 20, no. 11, pp. 1193-1200,2001.
 Zhu, R. Rangayyan, and A. Ells, "Detection of the optic nerve head in fundus images of the retina using the hough transform for circles," Journal of Digital Imaging, vol. 23, no. 3, pp. 332-341,2010.
 H. Li and 0. Chutatape, "Automated feature extraction in color retinal images by a model based approach," IEEE Transactions on Biomedical Engineering, vol. 51, no. 2, pp. 246-254,2004.
 Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels," IEEE Trans¬actions on Medical Imaging, vol. 22, no. 8, pp. 951-958,2003.
 M. Foracchia, E. Grisan, and A. Ruggeri, "Detection of optic disc in retinal images by means of a geometrical model of vessel structure," IEEE Trans. on Medical Imaging, vol. 23, no. 10, pp. 1189-1195,2004.
 A. Youssif, A. Ghalwash, and A. Ghoneim, "Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter," IEEE Transactions on Medical Imaging, vol. 27, no. 1, pp. 11-18,2008.
 J. Soares, J. Leandro, R. C. Jr., H. Jelinek, and M. Cree, "Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification," IEEE Trans. on Medical Imaging, vol. 25, no. 9, pp. 1214-1222,2006.
 J. Staal, M. Abramoff, M. Niemeijer, M. Viergever, and B. van Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Trans. on Medical Imaging, vol. 23, no. 4, pp. 501-509, 2004.
 Rubner, C. Tomasi, and L. Guibas, "The earth mover's distance as a metric for image retrieval," International Journal of Computer Vision, vol. 40, no. 2, pp. 99-121,2000.
 C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images," in International Conference on Computer Vision, Bombay, India, 1998, pp. 839 —846.