Automatic extraction of bronchus and centerline determination from CT images for three dimensional virtual bronchoscopy.

Law Tsui Ying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 64-70).Abstracts in English and Chinese.Acknowledgments --- p.iiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Structure of Bronchus --- p.3Chapter 1.2 --- Existing Systems --- p.4Chapter 1.2.1 --- Virtual Endoscope System (VES) --- p.4Chapter 1.2.2 --- Virtual Reality Surgical Simulator --- p.4Chapter 1.2.3 --- Automated Virtual Colonoscopy (AVC) --- p.5Chapter 1.2.4 --- QUICKSEE --- p.5Chapter 1.3 --- Organization of Thesis --- p.6Chapter 2 --- Three Dimensional Visualization in Medicine --- p.7Chapter 2.1 --- Acquisition --- p.8Chapter 2.1.1 --- Computed Tomography --- p.8Chapter 2.2 --- Resampling --- p.9Chapter 2.3 --- Segmentation and Classification --- p.9Chapter 2.3.1 --- Segmentation by Thresholding --- p.10Chapter 2.3.2 --- Segmentation by Texture Analysis --- p.10Chapter 2.3.3 --- Segmentation by Region Growing --- p.10Chapter 2.3.4 --- Segmentation by Edge Detection --- p.11Chapter 2.4 --- Rendering --- p.12Chapter 2.5 --- Display --- p.13Chapter 2.6 --- Hazards of Visualization --- p.13Chapter 2.6.1 --- Adding Visual Richness and Obscuring Important Detail --- p.14Chapter 2.6.2 --- Enhancing Details Incorrectly --- p.14Chapter 2.6.3 --- The Picture is not the Patient --- p.14Chapter 2.6.4 --- Pictures-'R'-Us --- p.14Chapter 3 --- Overview of Advanced Segmentation Methodologies --- p.15Chapter 3.1 --- Mathematical Morphology --- p.15Chapter 3.2 --- Recursive Region Search --- p.16Chapter 3.3 --- Active Region Models --- p.17Chapter 4 --- Overview of Centerline Methodologies --- p.18Chapter 4.1 --- Thinning Approach --- p.18Chapter 4.2 --- Volume Growing Approach --- p.21Chapter 4.3 --- Combination of Mathematical Morphology and Region Growing Schemes --- p.22Chapter 4.4 --- Simultaneous Borders Identification Approach --- p.23Chapter 4.5 --- Tracking Approach --- p.24Chapter 4.6 --- Distance Transform Approach --- p.25Chapter 5 --- Automated Extraction of Bronchus Area --- p.27Chapter 5.1 --- Basic Idea --- p.27Chapter 5.2 --- Outline of the Automated Extraction Algorithm --- p.28Chapter 5.2.1 --- Selection of a Start Point --- p.28Chapter 5.2.2 --- Three Dimensional Region Growing Method --- p.29Chapter 5.2.3 --- Optimization of the Threshold Value --- p.29Chapter 5.3 --- Retrieval of Start Point Algorithm Using Genetic Algorithm --- p.29Chapter 5.3.1 --- Introduction to Genetic Algorithm --- p.30Chapter 5.3.2 --- Problem Modeling --- p.31Chapter 5.3.3 --- Algorithm for Determining a Start Point --- p.33Chapter 5.3.4 --- Genetic Operators --- p.33Chapter 5.4 --- Three Dimensional Painting Algorithm --- p.34Chapter 5.4.1 --- Outline of the Three Dimensional Painting Algorithm --- p.34Chapter 5.5 --- Optimization of the Threshold Value --- p.36Chapter 6 --- Automatic Centerline Determination Algorithm --- p.38Chapter 6.1 --- Distance Transformations --- p.38Chapter 6.2 --- End Points Retrieval --- p.41Chapter 6.3 --- Graph Based Centerline Algorithm --- p.44Chapter 7 --- Experiments and Discussion --- p.48Chapter 7.1 --- Experiment of Automated Determination of Bronchus Algorithm --- p.48Chapter 7.2 --- Experiment of Automatic Centerline Determination Algorithm --- p.54Chapter 8 --- Conclusion --- p.62Bibliography --- p.6

Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Open-source virtual bronchoscopy for image guided navigation

This thesis describes the development of an open-source system for virtual bronchoscopy used in combination with electromagnetic instrument tracking. The end application is virtual navigation of the lung for biopsy of early stage cancer nodules. The open-source platform 3D Slicer was used for creating freely available algorithms for virtual bronchscopy. Firstly, the development of an open-source semi-automatic algorithm for prediction of solitary pulmonary nodule malignancy is presented. This approach may help the physician decide whether to proceed with biopsy of the nodule. The user-selected nodule is segmented in order to extract radiological characteristics (i.e., size, location, edge smoothness, calcification presence, cavity wall thickness) which are combined with patient information to calculate likelihood of malignancy. The overall accuracy of the algorithm is shown to be high compared to independent experts' assessment of malignancy. The algorithm is also compared with two different predictors, and our approach is shown to provide the best overall prediction accuracy. The development of an airway segmentation algorithm which extracts the airway tree from surrounding structures on chest Computed Tomography (CT) images is then described. This represents the first fundamental step toward the creation of a virtual bronchoscopy system. Clinical and ex-vivo images are used to evaluate performance of the algorithm. Different CT scan parameters are investigated and parameters for successful airway segmentation are optimized. Slice thickness is the most affecting parameter, while variation of reconstruction kernel and radiation dose is shown to be less critical. Airway segmentation is used to create a 3D rendered model of the airway tree for virtual navigation. Finally, the first open-source virtual bronchoscopy system was combined with electromagnetic tracking of the bronchoscope for the development of a GPS-like system for navigating within the lungs. Tools for pre-procedural planning and for helping with navigation are provided. Registration between the lungs of the patient and the virtually reconstructed airway tree is achieved using a landmark-based approach. In an attempt to reduce difficulties with registration errors, we also implemented a landmark-free registration method based on a balanced airway survey. In-vitro and in-vivo testing showed good accuracy for this registration approach. The centreline of the 3D airway model is extracted and used to compensate for possible registration errors. Tools are provided to select a target for biopsy on the patient CT image, and pathways from the trachea towards the selected targets are automatically created. The pathways guide the physician during navigation, while distance to target information is updated in real-time and presented to the user. During navigation, video from the bronchoscope is streamed and presented to the physician next to the 3D rendered image. The electromagnetic tracking is implemented with 5 DOF sensing that does not provide roll rotation information. An intensity-based image registration approach is implemented to rotate the virtual image according to the bronchoscope's rotations. The virtual bronchoscopy system is shown to be easy to use and accurate in replicating the clinical setting, as demonstrated in the pre-clinical environment of a breathing lung method. Animal studies were performed to evaluate the overall system performance