Abstract: A technique for prioritizing portions of compressed multi-resolution medical image data to be processed. More relevant portions of medical image data are compressed at a higher resolution than less relevant portions of medical image data. A mask is created from the image data and used to assign data compression quality factors to corresponding image blocks in the image data. The image data in each region is compressed based on their assigned quality factor. During processing of the compressed image data, the compressed image data and the mask are accessed. The mask is used to direct the processing of the compressed image data. Depending upon the application, the mask can be used to direct that the image data for the regions of most relevance be processed before the image data for the regions of lesser relevance. However, the mask can be used to direct that the image data for the regions of lesser relevance be processed before the image data for the regions of most relevance.

Abstract: A technique for selecting portions of a dynamic medical image data set to be stored and portions of the medical image data set to be discarded or processed differently in order to reduce the overall amount of image data that is stored for each image data set. The dynamic image data is segmented and registered. The segmentation and registration enables changes in intensity based on patient motion to be distinguished from changes in intensity due to anatomical function. In general, the regions with changes in intensity due to anatomical function are more relevant than regions with changes in intensity due to patient motion. The regions of greatest relevance are stored at their full resolution. Regions of lesser relevance are stored at less than the full resolution. Regions of no relevance are discarded and not stored at all.

Abstract: A technique for selecting portions of a multi-resolution medical image data set to be stored and the portions of the multi-resolution medical image data set to be discarded in order to reduce the overall amount of image data that is stored for each image data set. The selection is based on the clinical purpose for obtaining the medical image data. The clinical purpose for obtaining the medical image is used to define regions of interest in the medical image. At each resolution level of the multi-resolution medical image data set, the regions of interest are stored at the full resolution, while the remaining portions of the medical image are stored at a lesser resolution. A three-dimensional bit mask of the regions of interest is produced from a segmentation of the regions of interest. The segmentation list and the multi-resolution medical image data set are decomposed into multiple resolution levels. Each resolution level has a low frequency component and several high frequency components.

Abstract: A technique for selecting portions of a multi-resolution medical image data set to be stored and the portions of the multi-resolution medical image data set to be discarded in order to reduce the overall amount of image data that is stored for each image data set. The selection is based on the clinical purpose for obtaining the medical image data. The clinical purpose for obtaining the medical image is used to select a segmentation algorithm to segment the image data into several regions based on their relevance to the purpose for obtaining the image data. A mask is created from the segmented data. The mask is used to assign data compression quality factors to corresponding regions in the image data. The image data in each region is compressed based on their assigned quality factor. The image data that is highly relevant is compressed with lossless data compression. The image data in regions of lesser relevance are compressed with lossy data compression.

Abstract: A technique for selecting portions of a medical image data set to be stored and portions of the medical image data set to be discarded in order to reduce the overall amount of image data that is stored for each image data set. The selection is based on the clinical purpose for obtaining the medical image data. The clinical purpose for obtaining the medical image is used to select an atlas. The atlas is labeled with information that establishes the relative relevance of various regions of the atlas. The atlas is aligned to the image and the labeling information is transferred from the atlas to the medical image. Each region of the medical image is then processed based on the labeling information received from the atlas. The regions of greatest relevance are stored at their full resolution. Regions of lesser relevance are stored at less than the full resolution. Finally, regions of no relevance are discarded and not stored at all.

Abstract: The present invention provides a system for performing fast segmentation and image processing for enhanced three dimensional (3-D) visualization of a subject. The present invention automatically extracts unwanted dominant features from images while preserving the desired information. The present invention works especially well with Computed Tomography Angiograms (CTA) for viewing vasculature of a subject's head. Segmenting and removing dominant structures from image data permits visualization techniques such as maximum intensity projection (MIP), surface rendering and volume rendering, to provide enhanced vessel visualization.