A Discriminative Colour-to-Grayscale Representation for Retinal Vessel Segmentation
DOI:
https://doi.org/10.21152/1750-9548.13.1.73Abstract
Structural changes in retinal blood vessels are important indicators for many ocular diseases including diabetic retinopathy (DR), glaucoma, and hypertension. An automated vessel segmentation process almost always begins with acquired color fundus image, containing three layers of images (red, green and blue), and then quickly converted them to a single grayscale image. However, grayscale conversion is not unique and more than one grayscale representation can be obtained for a given color image. For the many currently existing automated vessel extraction methods, the green channel of the RGB color fundus image is routinely used as an input grey-scale representation to a pipeline of the segmentation process for the reason that it provides the best contrast among all three channels, namely red, green and blue. We hypothesize that vessel information contained in dropped channels, red and blue, will add to result in improved segmentation performance. In this paper, we propose a linear combination framework to utilize all three channels of the color fundus image based on their importance level rather than completely discarding some channels. We devised a Principal Component Analysis (PCA) method that provides appropriate weights for each color channel to realize a more discriminative grey-scale representation. The added information made available through PCA-based grayscale representation results in improved performance for Modautomated vessel segmentation algorithms. The performance of the framework is analyzed on two publically available databases (DRIVE, STARE) of fundus images quantifying improvements in all three aspects called accuracy, sensitivity, and specificity.
References
Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J., Retinal vessel Segmentation using the 2-D Gabor wavelet and supervised classification, IEEE Transactions on medical Imaging, 2006, 25(9), 1214-1222. https://doi.org/10.1109/tmi.2006.879967
Zhao, Y.Q., Wang, X.H., Wang, X.F., Shih, F.Y., Retinal vessels segmentation based on level set and region growing. Journal of Pattern Recognition, 2014, 47(7), 2437-2446. https://doi.org/10.1016/j.patcog.2014.01.006
Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A., Blood vessel segmentation methodologies in retinal images--a Survey, Computer methods and programs in biomedicine, 2012, 108(1), 407-433. https://doi.org/10.1016/j.cmpb.2012.03.009
U. T. Nguyen, A. Bhuiyan, L. A Park, and K. Ramamohanarao, An effective retinal blood vessel segmentation method using multi-scale line detection, Pattern Recognition, 2013, 46(3), 303-315. https://doi.org/10.1016/j.patcog.2012.08.009
M. Grundland and N. A. Dodgson, Decolorize: Fast, contrast enhancing, color to grayscale conversion, Pattern Recognition, 2007, 40(11), 2891–2896. https://doi.org/10.1016/j.patcog.2006.11.003
A. A. Gooch, S. C. Olsen, J. Tumblin, and B. Gooch, Color2gray: salience-preserving color removal, ACM Trans. Graph., 2005, 24(3), 634-639. https://doi.org/10.1145/1073204.1073241
K. Smith, P.-E. Landes, J. Thollot, and K. Myszkowski, Apparent greyscale: A simple and fast conversion to perceptually accurate images and video, Computer Graphics Forum, 2008, 27(2), 193-200. https://doi.org/10.1111/j.1467-8659.2008.01116.x
Ja-Won Seo; Seong Dae Kim, Novel PCA-based color-to-gray image conversion, 20th IEEE International Conf. on Image Processing (ICIP), 2013, 2279-2283. https://doi.org/10.1109/icip.2013.6738470
Ardizzone, E., Pirrone, R., Gambino, O., & Vitabile, S., Illumination correction on biomedical images, Computing and Informatics, 2014, 33(1), 175-196.
A. El-Zaart, Images thresholding using ISODATA techniques with gamma distribution, Pattern Recognition and Image Analysis, 2010, 20(1), 29-41. https://doi.org/10.1134/s1054661810010037
Published
How to Cite
Issue
Section
Copyright (c) 2019 M Khan, M Moatamedi, B Alzahabi
This work is licensed under a Creative Commons Attribution 4.0 International License.