Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
SIBGRAPI '01 Proceedings of the 14th Brazilian Symposium on Computer Graphics and Image Processing
An improved matched filter for blood vessel detection of digital retinal images
Computers in Biology and Medicine
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
IEEE Transactions on Image Processing
Computer Methods and Programs in Biomedicine
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
Computer-aided diagnosis of diabetic retinopathy: A review
Computers in Biology and Medicine
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Due to the importance of the matched filter in the automated detection of blood vessels in digital retinal images, improving its response is highly desirable. This filter may vary in many ways depending on the parameters that govern its response. In this paper, new parameters to optimize the sensitivity of the matched filter are found using genetic algorithms on the test set of the DRIVE databases. The area under the receiver operating curve (ROC) is used as a fitness function for the genetic algorithm. To evaluate the improved matched filter, the maximum average accuracy (MAA) is calculated to be 0.9422 and the average area under ROC is 0.9582.