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Proceedings Paper

Neural network ultrasound image analysis
Author(s): Alexander C. Schneider; David G. Brown; Mary S. Pastel
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Paper Abstract

Neural network based analysis of ultrasound image data was carried out on liver scans of normal subjects and those diagnosed with diffuse liver disease. In a previous study, ultrasound images from a group of normal volunteers, Gaucher's disease patients, and hepatitis patients were obtained by Garra et al., who used classical statistical methods to distinguish from among these three classes. In the present work, neural network classifiers were employed with the same image features found useful in the previous study for this task. Both standard backpropagation neural networks and a recently developed biologically-inspired network called Dystal were used. Classification performance as measured by the area under a receiver operating characteristic curve was generally excellent for the back propagation networks and was roughly comparable to that of classical statistical discriminators tested on the same data set and documented in the earlier study. Performance of the Dystal network was significantly inferior; however, this may be due to the choice of network parameter. Potential methods for enhancing network performance was identified.

Paper Details

Date Published: 14 September 1993
PDF: 8 pages
Proc. SPIE 1898, Medical Imaging 1993: Image Processing, (14 September 1993); doi: 10.1117/12.154550
Show Author Affiliations
Alexander C. Schneider, Stanford Univ. (France)
David G. Brown, Ctr. for Devices and Radiological Health/FDA (United States)
Mary S. Pastel, Ctr. for Devices and Radiological Health/FDA (United States)


Published in SPIE Proceedings Vol. 1898:
Medical Imaging 1993: Image Processing
Murray H. Loew, Editor(s)

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