Date of Award
Doctor of Philosophy
This dissertation presents the framework for analyzing and visualizing digital medical images. Two new segmentation methods have been developed: a probability based segmentation algorithm, and a segmentation algorithm that uses a fuzzy rule based system to generate "similarity" values for segmentation. A visualization software application has also been developed to effectively view and manipulate digital medical images on a desktop computer as well as in an immersive environment.;For the probabilistic segmentation algorithm, image data are first enhanced by manually setting the appropriate window center and width, and if needed a sharpening or noise removal filter is applied. To initialize the segmentation process, a user places a seed point within the object of interest and defines a search region for segmentation. Based on the pixels' spatial and intensity properties, a probabilistic selection criterion is used to extract pixels with a high probability of belonging to the object. To facilitate the segmentation of multiple slices, an automatic seed selection algorithm was developed to keep the seeds in the object as its shape and/or location changes between consecutive slices.;The second segmentation method, a new segmentation method using a fuzzy rule based system to segment tumors in a three-dimensional CT data was also developed. To initialize the segmentation process, the user selects a region of interest (ROI) within the tumor in the first image of the CT study set. Using the ROI's spatial and intensity properties, fuzzy inputs are generated for use in the fuzzy rules inference system. Using a set of predefined fuzzy rules, the system generates a defuzzified output for every pixel in terms of similarity to the object. Pixels with the highest similarity values are selected as tumor. This process is automatically repeated for every subsequent slice in the CT set without further user input, as the segmented region from the previous slice is used as the ROI for the current slice. This creates a propagation of information from the previous slices, used to segment the current slice. The membership functions used during the fuzzification and defuzzification processes are adaptive to the changes in the size and pixel intensities of the current ROI. The proposed method is highly customizable to suit different needs of a user, requiring information from only a single two-dimensional image.;Segmentation results from both algorithms showed success in segmenting the tumor from seven of the ten CT datasets with less than 10% false positive errors and five test cases with less than 10% false negative errors. The consistency of the segmentation results statistics also showed a high repeatability factor, with low values of inter- and intra-user variability for both methods.;The visualization software developed is designed to load and display any DICOM/PACS compatible three-dimensional image data for visualization and interaction in an immersive virtual environment. The software uses the open-source libraries DCMTK: DICOM Toolkit for parsing of digital medical images, Coin3D and SimVoleon for scenegraph management and volume rendering, and VRJuggler for virtual reality display and interaction. A user can apply pseudo-coloring in real time with multiple interactive clipping planes to slice into the volume for an interior view. A windowing feature controls the tissue density ranges to display. A wireless gamepad controller as well as a simple and intuitive menu interface control user interactions. The software is highly scalable as it can be used on a single desktop computer to a cluster of computers for an immersive multi-projection virtual environment. By wearing a pair of stereo goggles, the surgeon is immersed within the model itself, thus providing a sense of realism as if the surgeon is "inside" the patient.;The tools developed in this framework are designed to improve patient care by fostering the widespread use of advanced visualization and computational intelligence in preoperative planning, surgical training, and diagnostic assistance. Future work includes further improvements to both segmentation methods with plans to incorporate the use of deformable models and level set techniques to include tumor shape features as part of the segmentation criteria. For the surgical planning components, additional controls and interactions with the simulated endoscopic camera and the ability to segment the colon or a selected region of the airway for a fixed-path navigation as a full virtual endoscopy tool will also be implemented. (Abstract shortened by UMI.)
Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/
Jung Leng Foo
Foo, Jung Leng, "A framework for tumor segmentation and interactive immersive visualization of medical image data for surgical planning" (2008). Retrospective Theses and Dissertations. 15785.