Machine Learning in Medical Applications
Machine Learning and Its Applications, Advanced Lectures
A review of vessel extraction techniques and algorithms
ACM Computing Surveys (CSUR)
An approach to beacons detection for a mobile robot using a neural network model
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Computer Methods and Programs in Biomedicine
An approach to beacons detection for a mobile robot using a neural network model
MS '07 The 18th IASTED International Conference on Modelling and Simulation
International Journal of Computer Vision
Vascular tree segmentation in retinal angiographies: deformable contour model approach
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
Computer Methods and Programs in Biomedicine
A multi-population cooperative particle swarm optimizer for neural network training
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Blood vessel segmentation methodologies in retinal images - A survey
Computer Methods and Programs in Biomedicine
An approach to localize the retinal blood vessels using bit planes and centerline detection
Computer Methods and Programs in Biomedicine
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A neural-network classifier for detecting vascular structures in angiograms was developed. The classifier consisted of a multilayer feedforward network window in which the center pixel was classified using gray-scale information within the window. The network was trained by using the backpropagation algorithm with the momentum term. Based on this image segmentation problem, the effect of changing network configuration on the classification performance was also characterized. Factors including topology, rate parameters, training sample set, and initial weights were systematically analyzed. The training set consisted of 75 selected points from a 256×256 digitized cineangiogram. While different network topologies showed no significant effect on performance, both the learning process and the classification performance were sensitive to the rate parameters. In a comparative study, the network demonstrated its superiority in classification performance. It was also shown that the trained neural-network classifier was equivalent to a generalized matched filter with a nonlinear decision tree