Ensemble-based exudate detection in color fundus images Brigitta Nagy nagy.brigitta@inf.unideb.hu Faculty of Informatics, University of Debrecen Debrecen, Hungary Balázs Harangi harangi.balazs@inf.unideb.hu Faculty of Informatics, University of Debrecen Debrecen, Hungary Bálint Antal antal.balint@inf.unideb.hu Faculty of Informatics, University of Debrecen Debrecen, Hungary András Hajdu hajdu.andras@inf.unideb.hu Faculty of Informatics, University of Debrecen Debrecen, Hungary Abstract—Diabetic retinopathy causes blindness to millions in the world. Exudates are early lesions of this disease so the automatic detection is very important to slow down the progression of retinopathy. In this paper, an ensemble-based system is proposed to improve the detection. Optimal combination of preprocessing methods and exudate candidate extractors are found and organized into a voting system for this aim. Our results show that in this way we outperform the individual exudate detector algorithms. I. INTRODUCTION Diabetic retinopathy is a common cause of blindness especially in developed countries. However, at an early stage an appropriate treatment slow down the progression of this disease. Thus, the recognition of the early signs has great importance in corresponding automatic screening systems. Exudates are primary signs of diabetic retinopathy and occur when lipid or fat leaks from blood vessels or aneurysms. Exudates are light, small spots, which can have irregular shape, thus automatic exudate detection is a difficult task. We can find a large number of exudate detection algorithms in the medical image processing literature as see in [1]. These algorithms do not find all exudates and detect some false candidates, that is why we create a combination of these algorithms. The literature of ensemble-based systems suggests that the combination of individual algorithms built upon different principles tend to outperform individual accuracies. In this paper, we follow this principle with a complementary application of preprocessing methods. In other words, we will show how an optimal combination of <preprocessing method, candidate extractor> pairs can be organized into a voting system. The evaluation of the proposed method in a publicly available database shows that this approach can outperform the application of individual algorithms. The rest of the paper is organized as follows: we list the considered preprocessing methods and candidate extractors in section 2 and 3, respectively. Section 4 exhibits a stochastic search algorithm to find the optimal ensemble, and section 5 gives the creation of the voting system. Comparative results for the ensemble and the individual detectors are included in section 6, and conclusions are drawn in section 7. II. PREPROCESSING METHODS In this section, we present the preprocessing methods selected as possible participants of the ensemble. These preprocessing methods are applied on the images before performing the candidate extractors in order to enhance image contrast regarding exudates. The results of the preprocessing methods can be seen in Figure 1. • Illumination Equalization (IE) [2]: Pixels are adjusted in the following way: ) , ( ) , ( ) , ( c r I m c r I c r I w eq − + = , where I(r,c) is the original intensity value, m is the desired average intensity and I w(r,c) is the mean intensity value in a window. • Gray-World Normalization (GN) [2]: Avg new Avg new Avg new B b b and G g g R r r = = = , , , where (r, g, b) are the original intensity values, R Avg , G Avg and B Avg are the average intensity values in each band and (r new , g new , b new ) are the new values, respectively. • Division by an over-smoothed (DS) [3]: The mean of the intensity values is computed within a window. The original intensity value is divided by the mean value of its neighborhood. 7th International Symposium on Image and Signal Processing and Analysis (ISPA 2011) September 4-6, 2011, Dubrovnik, Croatia Special Sessions Image and Signal Analysis for Computational Life Sciences 700