Principles of Image Processing in Digital Chest Radiography Mathias Prokop*, Ulrich Neitzel†, and Cornelia Schaefer-Prokop‡ Summary: Image processing has a major impact on image quality and diagnostic performance of digital chest radiographs. Goals of processing are to reduce the dy- namic range of the image data to capture the full range of attenuation differences between lungs and mediastinum, to improve the modulation transfer function to op- timize spatial resolution, to enhance structural contrast, and to suppress image noise. Image processing comprises look-up table operations and spatial filtering. Look-up table operations allow for automated signal normalization and arbitrary choice of image gradation. The most simple and still widely applied spatial filtering algorithms are based on unsharp masking. Various modifications were introduced for dynamic range reduction and MTF restoration. More elaborate and more effective are multi- scale frequency processing algorithms. They are based on the subdivision of an image in multiple frequency bands according to its structural composition. This allows for a wide range of image manipulations including a size-independent enhancement of low-contrast structures. Principles of the various algorithms will be explained and their impact on image appearance will be illustrated by clinical examples. Optimum and sub-optimum parameter settings are discussed and pitfalls will be explained. INTRODUCTION Digital chest radiography has now come of age. Al- most 2 decades have passed since the introduction of the first digital storage phosphor systems (Computed Radi- ography, CR) in the early 1980s. Digital image process- ing has always been an integral part of digital radiogra- phy, 1 but most users are hardly aware of the processing techniques integrated in their systems. The term “post- processing” is commonly associated with the processing option available for the user and is often distinguished from the default processing that all digital radiographs are subjected to. In reality, this distinction is arbitrary since available processing options are generally identi- cal. In an ideal environment, the default processing should be chosen so that no additional “postprocessing” is necessary. This article will explain principles of the various im- age processing algorithms and will illustrate their impact on clinical chest radiographs. Adequate parameter set- tings are suggested, sub-optimum parameter settings are discussed and pitfalls will be explained. WHY IMAGE PROCESSING? Digital radiography systems are characterized by a very wide dynamic range and linear response to the in- cident radiation. They can therefore capture the wide attenuation differences between lungs and mediastinum and are much less vulnerable to changes in exposure dose than conventional screen-film radiographs. 2 If no further processing were employed and the images cap- tured by the detector systems were directly transformed into gray levels on a viewing monitor or laser film, the resulting image would be characterized by a good trans- parency of the mediastinum but otherwise would appear extremely “gray” because of a lack of contrast. At the same time, image sharpness may not be as good as in screen-film systems because of limitations due to pixel size and less favorable detector characteristics. For this reason, all digital radiographs are subjected to From *The Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands; †Philips Medical Systems, Ham- burg, Germany; and ‡The Department of Radiology, University of Vienna, Austria. Address correspondence and reprint requests to Mathias Prokop, MD, University Medical Center Utrecht, Department of Radiology, Heidelberglaan 100, Utrecht NL-3508 GA, The Netherlands. E-mail: m.prokop@azu.nl Journal of Thoracic Imaging 18:148–164 © 2003 Lippincott Williams & Wilkins, Inc., Philadelphia 148