Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Experiments in colour texture analysis
Pattern Recognition Letters
Digital Image Processing
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of Colour Spaces for Optic Disc Localisation in Retinal Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
The Colour Image Processing Handbook (Optoelectronics, Imaging and Sensing)
Comparative pixel-level exudate recognition in colour retinal images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Gabor parameter selection for local feature detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
A hybrid intelligent system for medical data classification
Expert Systems with Applications: An International Journal
Detection and classification of retinal lesions for grading of diabetic retinopathy
Computers in Biology and Medicine
Automated detection of exudates and macula for grading of diabetic macular edema
Computer Methods and Programs in Biomedicine
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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 amethod 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.