An artificial neural network for lesion detection on single-photon emission computed tomographic images.
Journal Article (Journal Article)
RATIONALE AND OBJECTIVES: An artificial neural network (ANN) has been developed to detect nonactive circular lesions on single-slice, single-photon emission computed tomographic (SPECT) images reconstructed using filtered back projection (FBP). METHODS: The neural network is a single-layer perception which learns to identify features on the SPECT image using supervised training with a modified delta rule. The network was trained on a set of SPECT images containing clinically realistic levels of noise. The trained network was applied to a set of 120 images, and the detection performance was evaluated at several decision thresholds using receiver operating characteristic (ROC) analysis. RESULTS: The trained neural network performed better than human observers for the same detection task with the same images as reflected by a significantly larger ROC curve area. CONCLUSIONS: ANN can be trained successfully to perform lesion detection on reconstructed SPECT images.
Full Text
Duke Authors
Cited Authors
- Floyd, CE; Tourassi, GD
Published Date
- September 1992
Published In
Volume / Issue
- 27 / 9
Start / End Page
- 667 - 672
PubMed ID
- 1399448
International Standard Serial Number (ISSN)
- 0020-9996
Digital Object Identifier (DOI)
- 10.1097/00004424-199209000-00001
Language
- eng
Conference Location
- United States