Multispectral image classification techniques, originally developed for satellite imaging, have recently been applied to MR tissue characterization. Statistical assessment of multispectral tissue classification techniques has been used to select the most promising of several alternative methods. Normal and pathological images have been analyzed using multispectral analysis and image classification. These synthetic maps are then compared pixel by pixel with manually prepared classification maps of the same MR images. Using these methods, the authors have found that both supervised and unsupervised classification techniques yielded theme maps (class maps) which demonstrated tissue characteristic signatures and tissue classification errors found in computer-generated theme maps were due to subtle graph scale changes present in the original MR data sets arising from radiometric inhomogeneity and spatial nonuniformity.
|Number of pages||28|
|Journal||Critical Reviews in Biomedical Engineering|
|Publication status||Published - 1987|
ASJC Scopus subject areas
- Biomedical Engineering