Methods of bronchial tree reconstruction and camera distortion corrections for virtual endoscopic environments.
The use of three-dimensional visualization of anatomical structures in diagnostics and medical training is growing. The main components of virtual respiratory tract environments include reconstruction and simulation algorithms as well as correction methods of endoscope camera distortions in the case of virtually-enhanced navigation systems. Reconstruction methods rely usually on initial computer tomography (CT) image segmentation to trace contours of the tracheobronchial tree, which in turn are used in the visualization process. The main segmentation methods, including relatively simple approaches such as adaptive region-growing algorithms and more complex methods, e.g. hybrid algorithms based on region growing and mathematical morphology methods, are described in this paper. The errors and difficulties in the process of tracheobronchial tree reconstruction depend on the occurrence of distortions during CT image acquisition. They are usually related to the inability to exactly fulfil the sampling theorem's conditions. Other forms of distortions and noise such as additive white Gaussian noise, may also appear. The impact of these distortions on the segmentation and reconstruction may be diminished through the application of appropriately selected image prefiltering, which is also demonstrated in this paper. Methods of surface rendering (ray-casting, ray-tracing techniques) and volume rendering will be shown, with special focus on aspects of hardware and software implementations. Finally, methods of camera distortions correction and simulation are presented. The mathematical camera models, the scope of their applications and types of distortions were have also been indicated.
Socha
Mirosław
M
AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, Poland. mireksocha@op.pl
Duplaga
Mariusz
M
Turcza
Paweł
P
D000465:Algorithms; D001980:Bronchi / Q000033:anatomy & histology*; D001999:Bronchoscopy / Q000379:methods*; D003936:Diagnosis, Computer-Assisted; D004501:Education, Medical; D006801:Humans; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed; D014584:User-Computer Interface