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Automated detection of circinate exudates in retina digital images using empirical mode decomposition and the entropy and uniformity of the intrinsic mode functions

  • Salim Lahmiri EMAIL logo and Mounir Boukadoum
Published/Copyright: March 11, 2014

Abstract

This work presents a new automated system to detect circinate exudates in retina digital images. It operates as follows: the true color image is converted to gray levels, and contrast-limited adaptive histogram equalization (CLAHE) is applied to it before undergoing empirical mode decomposition (EMD) as intrinsic mode functions (IMFs). The entropies and uniformities of the first two IMFs are then computed to form a feature vector that is fed to a support vector machine (SVM) for classification. The experimental results using a set of 45 images (23 normal images and 22 images with circinate exudates taken from the STARE database) and tenfold cross-validation indicate that the proposed approach outperforms previous works found in the literature, with perfect classification. In addition, the image processing time was <4 min, making the presented circinate exudate detection system fit for use in a clinical environment.


Corresponding author: Salim Lahmiri, Department of Computer Science, University of Quebec at Montreal, 201 President-Kennedy, Local PK-4150, Montréal (Québec) H2X 3Y7, Canada, E-mail:

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Received: 2013-4-19
Accepted: 2014-2-11
Published Online: 2014-3-11
Published in Print: 2014-8-1

©2014 by De Gruyter

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