Lesion size quantification in SPECT using an artificial neural network classification approach.

Journal Article (Journal Article)

An artificial neural network (ANN) has been developed to determine the size of lesions detected in single photon emission computed tomographic images. The network is the Learning Vector Quantizer and is trained to perform size quantification based on image neighborhoods extracted around the lesions. The ANN is compared to the optimal, Bayesian algorithm developed to perform the same task using the unreconstructed, projection data. The performance of the neural network is evaluated at two different noise levels. The Bayesian algorithm provides the upper bound for size quantification performance against which the ANN is compared. In the ideal case where the Bayesian algorithm has explicit knowledge of the underlying distributions, its performance is superior to that of the neural network. However, in the more realistic case where the distributions need to be estimated from the same learning sample the ANN was trained on, the two algorithms have comparable performances.

Full Text

Duke Authors

Cited Authors

  • Tourassi, GD; Floyd, CE

Published Date

  • June 1995

Published In

Volume / Issue

  • 28 / 3

Start / End Page

  • 257 - 270

PubMed ID

  • 7554859

International Standard Serial Number (ISSN)

  • 0010-4809

Digital Object Identifier (DOI)

  • 10.1006/cbmr.1995.1017


  • eng

Conference Location

  • United States