Image database clustering to improve exudate detection in color fundus images

In this paper a novel approach to improve exudate detection in color fundus images is proposed. Image databases usually contain images with different characteristics, thus determining an optimal parameter setting of an algorithm is a challenging task. To overcome this problem we cluster the image da...

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Bibliographic Details
Published in2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 727 - 731
Main Authors Nagy, Brigitta, Antal, Balint, Hajdu, Andras
Format Conference Proceeding
LanguageEnglish
Published University of Trieste and University of Zagreb 01.09.2013
Subjects
Clustering algorithms
Detectors
Image color analysis
Image databases
Retina
Sensitivity
Signal processing algorithms
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Summary:In this paper a novel approach to improve exudate detection in color fundus images is proposed. Image databases usually contain images with different characteristics, thus determining an optimal parameter setting of an algorithm is a challenging task. To overcome this problem we cluster the image databases. For each cluster an optimal parameter setting is determined for the same algorithm. We extract Haralick features from the image, and apply k-means clustering to obtain the clusters. We tested our approach on a publicly available database, where the proposed approach improved the performance of a state-of-the-art exudate detector.
ISSN:1845-5921
DOI:10.1109/ISPA.2013.6703833