Lesion size quantification in SPECT using an artificial neural network classification approach.
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.
Tourassi
G D
GD
Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA.
Floyd
C E
CE
D000465:Algorithms; D016477:Artifacts; D001499:Bayes Theorem; D002965:Classification; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008961:Models, Structural; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013667:Technetium; D015899:Tomography, Emission-Computed, Single-Photon
D013667:Technetium