Abstract
The techniques of statistical pattern recognition are implemented to determine the best combination of tissue characterization parameters for maximizing the diagnostic accuracy of a given task. In this paper, we considered combinations of four ultrasonic tissue parameters to discriminate between normal liver and chronic hepatitis. The separation between normal and diseased samples was made by application of the Bayes test for minimum risk which minimizes the error rate for classifying tissue states while including the prior probability for the presence of disease and the cost of misclassification. Large differences in classification performance of various tissue parameter combinations were demonstrated by ROC analysis. The power of additional features to classify tissue states, even those derived from other imaging modalities, can be compared directly in this manner.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 24-31 |
| Number of pages | 8 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 626 |
| DOIs | |
| State | Published - Jun 12 1986 |
| Externally published | Yes |
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering