Fundamentals of digital image processing
Fundamentals of digital image processing
A simple approach for the estimation of circular arc center and its radius
Computer Vision, Graphics, and Image Processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Comparative Exudate Classification Using Support Vector Machines and Neural Networks
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images
Journal of Medical Systems
Identification of the optic nerve head with genetic algorithms
Artificial Intelligence in Medicine
Simple and Robust Optic Disc Localisation Using Colour Decorrelated Templates
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Comparative pixel-level exudate recognition in colour retinal images
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Journal of Medical Systems
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
Computers in Biology and Medicine
Identification of anatomic retinal structures for macular delineation in fluorescein angiograms
Integrated Computer-Aided Engineering
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Retinal exudates are a characteristic feature of many retinal diseases such as Diabetic Retinopathy. We address the development of a method to quantitatively diagnose these random yellow patches in colour retinal images automatically. After a colour normalisation and contrast enhancement preprocessing step, the colour retinal image is segmented using Fuzzy C-Means clustering. We then classify the segmented regions into two disjoint classes, exudates and non-exudates, comparing the performance of various classifiers. We also locate the optic disk both to remove it as a candidate region and to measure its boundaries accurately since it is a significant landmark feature for ophthalmologists. Three different approaches are reported for optic disk localisation based on template matching, least squares arc estimation and snakes. The system could achieve an overall diagnostic accuracy of 90.1% for identification of the exudate pathologies and 90.7% for optic disk localisation.