Three-dimensional visualization system as an aid for lung cancer detection

The purpose of the work was to create a three-dimensional (3-D) visualization system to aid physicians in observing abnormalities of the human lungs. A series of 20-30 helical CT lung slice images (typical reconstruction interval: 6-10 mm) obtained from the lung cancer screening protocol as well as a series of 100-150 diagnostic helical CT lung slice images (typical reconstruction interval: 1-2 mm) were used as an input. We designed a segmentation filter to enhance the lung boundaries and filter out small and medium bronchi from the original images. The pairs of original and filtered images were further processed with the contour extraction method to segment out only the lung field for further study. In the next step the segmented lung images containing the small bronchi and lung textures were used to generate the volumetric dataset input for the three-dimensional visualization system. Additional processing for the extracted contour was used to smooth the 3-D lung contour in order to eliminate edge discontinuities related to bronchi as well as abnormalities (e.g. nodules) located close to the lung boundaries. The computer program developed allows, among others, viewing of the three-dimensional lung object from various angles, zooming in and out as well as selecting the regions of interest for further viewing. The density and gradient opacity tables are defined and used to manipulate the displayed contents of 3-D rendered images. Thus, an effective "see-through" technique is applied to the 3-D lung object for better visual access to the internal lung structures like bronchi and possible cancer masses. These and other features of the resulting 3-D lung visualization system give the user (physician) a powerful tool to observe and investigate the patient's lungs. The filter designed for this study is a completely new solution that greatly facilitates the boundary detection. The developed three-dimensional visualization system dedicated from chest CT provides the user a new way to explore effective diagnosis of potential lung abnormalities and cancer. In the authors' opinion, the developed system can be successfully used to view and analyze patient's lung CT images in a new powerful approach in both diagnosis and surgery-planning applications. Additionally, we see the possibility of using the system for teaching anatomy as well as pathology of the human lung.