A neural network (10) has been optimized to function as an image preprocessor. The image processor (170) evaluates input imagery and outputs regions of interest (Fig. 11), ignoring backgrounds or data features that differ from programmed geometries. The smart imager algorithm has been applied to medical and military datasets. Results from over 200 patient images demonstrate that the image preprocessor can reliably isolate information of diagnostic interest in pulmonary data. Similarly, a smart preprocessor reliably locates peaks in correlation surfaces in an automated target recognition application. In both cases, the smart imager is able to ignore noisy artifacts and background information, highlight features of interest andimprove detection system performance.
展开▼
