[Computer aided diagnosis in chest radiology - current topics and techniques].
The proliferation of digital data sets and the increasing amount of images, e. g. through the use of multislice spiral CT or multiple follow-up examinations in the context of new therapies, are ideal prerequisites for computer-aided diagnosis (CAD) in chest radiology. Multiple studies have described the applications and advantages of computer assistance in performing different diagnostic tasks. More powerful computers will enable the introduction of these systems into the clinical routine and could provide an enormous increase in morphological and functional information. The commercial introduction of tools for detection and visualization of pulmonary nodules has already begun. This is one of the most widely-reported applications in view of the ongoing studies on lung cancer screening. The next generation of tools will improve the diagnosis of emphysema through detection, quantification and classification. Many more uses are being developed, for instance the detection and classification of infiltrates, volume measurements or functional pulmonary imaging (e. g. dynamic ventilation CT or (3)Helium-MRI). Grossly simplified, most systems use a three level structure consisting of segmentation/feature extraction, classification of extracted features and an output unit. The output can be mere visualization through color-coding, volume measurements or calculated probabilities. The output supports the radiologist in establishing his findings and preparing differential and final diagnoses as well as providing quantitative data for follow-up studies. Different techniques are used for segmentation of lung areas as the basis for a variety of applications. Some commonly-used techniques for this and other tasks are density masks and threshold-based algorithms. Data processing is predominantly carried out with Bayesian classifiers or neural networks. This article describes the current status of research and provides insight into the common schemes and capabilities of the systems. It focuses particularly on common topics such as segmentation, volume measurement, detection of pulmonary nodules, quantification of emphysema and analysis of ground glass opacities.
Achenbach
T
T
Klinik und Poliklinik für Radiologie, Johannes-Gutenberg-Universität Mainz.
Vomweg
T
T
Heussel
C P
CP
Thelen
M
M
Kauczor
H U
HU
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008168:Lung / Q000000981:diagnostic imaging; D013902:Radiography, Thoracic / Q000379:methods*