Computer-aided classification of magnetic-resonance images
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abstract: We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with her best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. The system's components are a preprocessing module for normalizing images, an unsupervised clustering algorithm (ISODATA), a maximum-likelihood classifier, and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. The system is best thought of as a data-reduction tool, rather than as an expert system; it highlights salient features of the image for the clinician without requiring user intervention.
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| Abstract | We describe the design, implementation, an … We describe the design, implementation, and preliminary evaluation of a computer system to aid clinicians in the interpretation of cranial magnetic-resonance images. The system classifies normal and pathologic tissues in a test set of MR scans with high accuracy. It also provides a simple, rapid means whereby an unassisted expert may reliably label an image with her best judgment of its histologic composition, yielding a gold-standard image; this step facilitates objective evaluation of classifier performance. The system's components are a preprocessing module for normalizing images, an unsupervised clustering algorithm (ISODATA), a maximum-likelihood classifier, and an evaluation module based on confusion-matrix generation. The algorithms for classifier evaluation and gold-standard acquisition are advances over previous methods. The system is best thought of as a data-reduction tool, rather than as an expert system; it highlights salient features of the image for the clinician without requiring user intervention. ician without requiring user intervention. |
| Author | Edward Herskovits +, and Michael Walker + |
| Bibtype | techreport + |
| Institution | Knowledge Systems, AI Laboratory + |
| Key | KSL-89-47 + |
| Month | May + |
| Number | KSL-89-47 + |
| Tag | Computer science + |
| Title | Computer-Aided Classification of Magnetic-Resonance Images + |
| Tr id | KSL-89-47 + |
| Year | 1989 + |
