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There are many aspects that influence and deteriorate the detection of pathologies in X-ray images. Some of those are due to effects taking place in the stage of forming the X-ray intensity pattern in front of the x-ray detector. These can be described as motion blurring, depth blurring, anatomical background, scatter noise and structural noise. Structural noise results from an overlapping of fine irrelevant anatomical structures. A method for measuring the combined effect of structural noise and scatter noise was developed and will be presented in this paper. This method is based on the consideration that within a pair of projections created after rotation of the object with a small angle (which is within the typical uncertainty in positioning the patient) both images would show the same relevant structures whereas the projection of the fine overlapping structures will appear quite differently in the two images. To demonstrate the method two X-ray radiographs of a lung phantom were produced. The second radiograph was achieved after rotating the lung by an angle of about 3. Dyadic wavelet representations of both images were regarded. For each value of the wavelet scale parameter the corresponding pair of approximations was matched using the cross correlation matching technique. The homologous regions of approximations were extracted. The image containing only those structures that appear in both images simultaneously was then reconstructed from the wavelet coefficients corresponding to the homologous regions. The difference between one of the original images and the noise-reduced image contains the structural noise and the scatter noise.

A physical characterization for different modern digital detectors (transparent imaging plate, CsI flat panel detector) as well as of an analogue screen-film system, a selenium based system and a 'normal CR system are presented. The detectable information in radiographs produced with digital systems is strongly dependent on the image processing used. The effect of an optimization of this processing corresponding to the physical parameters of different detectors was evaluated. To do so, VGA1, 2 and MS-ROC3 have been used. All optimizations led to 'better' images concerning the information content which can be used by the radiologist. For the flat-panel detector it led to a reduction of images which must be manually postprocessed and even of repeated radiographs. This was achieved by using an adaptive autowindow algorithm which is able to distinguish between anatomical structures and implants. For another detector the detection of unsharp lung nodules could be improved by far. Information can not be added by image processing, it is only possible to make it better visible. To do so, the optimizing of the processing for each kind of detector is much more important than an optimization concerning different organ programs.

There is a lot of effort to develop digital detectors for mammography, for example for the screening programs. For this development it would be very helpful to know, which structure sizes have to be reproduced. Besides that, the information content of images produced by digital systems may be influenced by aliasing artifacts, if there are frequency components in the incoming signal higher than the Nyquist- frequency of the detector. Therefore the spatially modulated pattern of the X-ray intensity in a mammogram has to be known, but so far only little information is available. The method to measure and analyze the X-ray intensity pattern of a radiograph of the thorax in the detector plane which was presented at 'Medical Imaging' conference last year has been further developed in a way, that it meets the requirements for determining the intensity pattern in mammography.