Enhancement of chest images by automatic adaptive spatial filtering

Postprocessing of the image data is an exciting capability of digital radiography that may improve diagnostic performance. We present a new algorithm that selectively enhances edges and contrast in both lungs and mediastinum while minimally amplifying noise in chest images. Using different size kernels, two smoothed images are generated from the original chest image. The two regions of interest (lungs and mediastinum) are identified based on the distribution of pixel values in the image. A modified nonlinear unsharp mask subtraction technique is then applied. The resulting image has enhanced high- and middle-frequency information in the mediastinum without distorting lung parenchyma or significantly enhancing noise. We consider that the technique employed in this study could be suitable for routine use although its true effectiveness in improving diagnostic accuracy awaits observer-performance evaluation that is currently under way.