With the rapid development of medical imaging devices and related techniques, the resolution of the acquired images are getting higher and higher, which leads to very large (out-of-core) medical data sets and challenges the conventional medical image processing and analyzing algorithms. Furthermore, most mainstream algorithm toolkits and application systems do not provide the supports for out-of-core data processing, which makes these toolkits loose the function of supporting platform for the algorithm research and development in the out-of-core situation and restrict their application for very large medical data sets which appear more and more these years. The research works of this dissertation just aim at above problems and include following parts: 1. Proposed a set of algorithm frameworks for out-of-core medical data processing and visualization including the design of the total framework, access interfaces and underlying data structures, implemented them in our former basic algorithm platform, and completed the design and implementation of the new out-of-core medical data processing and analyzing algorithm toolkit MITK (Medical Imaging ToolKit) 2.0. 2. Proposed a 3D human computer interaction framework based on 3D widgets for out-of-core medical data visualization and implemented it in the new out-of-core medical data processing and analyzing algorithm platform. It not only provides a comparatively independent, simple-to-use and extensible 3D interaction framework for the out-of-core medical data visualization algorithms, but also sets up a communicating bridge between the qualitative display of the visualization algorithms and the quantitative analysis needed by actual applications. 3. Proposed an efficient out-of-core volume ray casting algorithm for the visualization of very large (out-of-core) medical data sets based on semi-adaptive partitioning and graphics hardware acceleration. It applied a semi-adaptive partitioning strategy to split the original volume data into sub-blocks with appropriate size and separate the background region as exact as possible. BSP tree is also used to organize all the sub-blocks so as to ensure that all the sub-blocks are rendered in front-to-back sequence and avoid random accesses to the original data. Furthermore, when rendering the BSP tree, the empty sub-blocks are skipped and GPU is used to accelerate ray casting computation in each nonempty sub-block. 4. Proposed a 3D texture-based out-of-core volume rendering algorithm for accelerating the visualization of very large medical data sets. In order to render out-of-core volume data sets with limited 3D texture buffer, this algorithm divides the volume data into small blocks, applies 3D texture hardware and an improved method for generating texture polygons to optimize the rendering process of each block, so as to accelerate the total rendering procedure enormously.
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