NDLI: Back-propagation network and its configuration for blood vessel detection in angiograms

Content Provider	IEEE Xplore Digital Library
Author	Nekovei, R. Ying Sun
Copyright Year	1990
Abstract	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/spl times/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.<>
Sponsorship	IEEE Computational Intelligence Society
Starting Page	64
Ending Page	72
Page Count	9
File Size	949041
File Format	PDF
ISSN	10459227
Volume Number	6
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-01-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blood vessels Network topology Biomedical imaging Nonhomogeneous media Gray-scale Backpropagation algorithms Image segmentation Classification tree analysis Matched filters Decision trees
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Computer Science Applications Software

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