A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images.
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.
Osareh
Alireza
A
Computer Science Department, Engineering Faculty, Shahid Chamran University, Ahvaz 61357-83151, Iran. alireza.osareh@scu.ac.ir
Shadgar
Bita
B
Markham
Richard
R
D000465:Algorithms; D016000:Cluster Analysis; D003930:Diabetic Retinopathy / Q000473:pathology*; D005122:Exudates and Transudates / Q000737:chemistry*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016571:Neural Networks, Computer; D016011:Normal Distribution; D012160:Retina / Q000473:pathology