A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images

Abstract

Currently, there is an increasing interest for setting up medical systems that can screen a large number of people for sight threatening diseases, such as diabetic retinopathy. This paper presents a method for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques. The color retinal images are segmented using fuzzy c-means clustering following some preprocessing steps, i.e., color normalization and contrast enhancement. The entire segmented images establish a dataset of regions. To classify these segmented regions into exudates and nonexudates, a set of initial features such as color, size, edge strength, and texture are extracted. A genetic-based algorithm is used to rank the features and identify the subset that gives the best classification results. The selected feature vectors are then classified using a multilayer neural network classifier. The algorithm was implemented using a large image dataset consisting of 300 manually labeled retinal images, and could identify affected retinal images with 96.0% sensitivity while it recognized 94.6% of the normal images, i.e., the specificity. Moreover, the proposed scheme illustrated an accuracy including 93.5% sensitivity and 92.1% predictivity for identification of retinal exudates at the pixel level.

Refining Coarse Manual Segmentations with Stable Probability Regions

“…Osareh et al [10] segment candidate regions from fuzzy c-means clustering (FCM) using a bank of Gabor filters and identify the best feature subset with genetic optimization. The extracted regions are classified by a multilayer perceptron neural network (NN).…”

Identification of Exudates Using Fuzzy Mathematical Morphology

“…The exponential function (Equation 4) produces significant enhancement when the contrast is low, while it provides less enhancement if the contrast is already high. The examples of retinal images after the contrast enhancement (Osareh et al, 2009). While the contrast enhancement improves the contrast of exudates, it may also enhance the contrast of some non-exudates background pixels, so that these pixels can wrongly be identified as exudates.…”

An Efficient Approach for Detection of Exudates in Diabetic Retinopathy Images Using Clustering Algorithm

“…Alireza Osareh et al [2] proposed a method for automatic identification of exudates based on computational Intelligence technique The colour retinal images were segmented using fuzzy c-means clustering. Feature vector were extracted and classified using multilayer neural network classifier.…”

“…It is a major cause of blindness in both middle and advanced age groups. The International Diabetes Federation reports that over 50 million people in India have this disease and it is growing rapidly (IDF 2009a) [2]. The estimated prevalence of diabetes for all age groups worldwide was 2.8% in 2000 and 4.4% in 2030 meaning that the total number of diabetes patients is forecasted to rise from 171 million in 2000 to 366 million in 2030 [3].…”