Adaptive thresholding of chest temporal subtraction images in computer-aided diagnosis of pathologic change.

Radiologists frequently use chest radiographs acquired at different times to diagnose a patient by identifying regions of change. Temporal subtraction (TS) images are formed when a computer warps a radiographic image to register and then subtract one image from the other, accentuating regions of change. The purpose of this study was to create a computer-aided diagnostic (CAD) system to threshold chest TS images and identify candidate regions of pathologic change. Each thresholding technique created two different candidate regions: light and dark. Light regions have a high gray-level mean, while dark regions have a low gray-level mean; areas with no change appear as medium-gray pixels. Ten different thresholding techniques were examined and compared. By thresholding light and dark candidate regions separately, the number of properly thresholded regions improved. The thresholding of light and dark regions separately produced fewer overall candidate regions that included more regions of actual pathologic change than global thresholding of the image. Overall, the moment-preserving method produced the best results for light regions, while the normal distribution method produced the best results for dark regions. Separation of light and dark candidate regions by thresholding shows potential as the first step in creating a CAD system to detect pathologic change in chest TS images.