Abstract: A method and system for incorporating voice commands into the interactive process of image segmentation. Interactive image segmentation involves a user pointing at an image; voice commands quicken this interaction by indicating the purpose and function of the pointing. Voice commands control the governing parameters of the segmentation algorithm. Voice commands guide the system to learn from the user's actions, and from the user's manual edits of the results from automatic segmentation.

Abstract: A method and system for incorporating voice commands into the interactive process of image segmentation. Interactive image segmentation involves a user pointing at an image; voice commands quicken this interaction by indicating the purpose and function of the pointing. Voice commands control the governing parameters of the segmentation algorithm. Voice commands guide the system to learn from the user's actions, and from the user's manual edits of the results from automatic segmentation.

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: 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.

Abstract: A method for visualizing data includes the step of providing a virtual flight visualization of image data representing a non-landscape object.

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 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 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: Certain embodiments of the present invention provide methods and systems for selecting an application for processing an electronic image file based upon the content of the image file. Certain embodiments provide an image selector selecting an image from an image storage device, an identification module providing a label to the image based on the content of the image, an application selector choosing at least one application from a plurality of applications to process the image, and a learning unit maintaining a record of image labels and the applications presently and previously processing the images.

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: System and method of generating a warp field to generate a deformed image. The system and method use segmentation in a new method of image deformation with the intent of improving the anatomical significance of the results. Instead of allowing each image voxel to move in any direction, only a few anatomical motions are permissible. The planning image and the daily image are both segmented automatically. These segmentations are then analyzed to define the values of the few anatomical parameters that govern the allowable motions. Given these model parameters, a deformation or warp field is generated directly without iteration. The warp field is applied to the planning image or the daily image to deform the image. The deformed image can be displayed to a user.

Abstract: Methods and system for segmenting image data are provided. The method includes fitting a topological atlas to acquired image data and generating spatially varying priors based on the fitted topological atlas. The method further includes segmenting the acquired image data based on the spatially varying priors.

Abstract: A computer-aided detection system for evaluating tissue based on a series of timed images acquired both before and after a contrast agent is administered performs a temporal analysis of the tissue and then a spatial analysis in which the tissue is categorized. After the temporal and spatial analysis is performed, the results can be displayed. The displayed results can include both tissue characterization results, underlying curves used to determine the characterization, and confidence data regarding how good the characterization is expected to be. The confidence data can be provided, for example, by using variations in color schemes, displaying numerical confidence levels, or providing graphical features such as piecewise linear models.

Abstract: A technique for reconstructing a corrected MR image from MR images distorted by foreign object induced magnetic fields includes locating a foreign object in a subject and defining a localized area of a field of view about the foreign object where a magnetic field distortion adversely affects a first magnetic distortion correction technique. The first magnetic distortion correction technique is applied to the field of view other than in the localized area. A second magnetic distortion correction technique is applied to the localized area and the results of the application of the first and second magnetic distortion correction techniques are combined. An image is reconstructed based on the results of the application of the first and second magnetic distortion correction techniques.

Abstract: A technique for reconstructing a corrected MR image from MR images distorted by foreign object induced magnetic fields includes locating a foreign object in a subject and defining a localized area of a field of view about the foreign object where a magnetic field distortion adversely affects a first magnetic distortion correction technique. The first magnetic distortion correction technique is applied to the field of view other than in the localized area. A second magnetic distortion correction technique is applied to the localized area and the results of the application of the first and second magnetic distortion correction techniques are combined. An image is reconstructed based on the results of the application of the first and second magnetic distortion correction techniques.

Abstract: Certain embodiments of the present invention provide methods and systems for selecting an application for processing an electronic image file based upon the content of the image file. Certain embodiments provide an image selector selecting an image from an image storage device, an identification module providing a label to the image based on the content of the image, an application selector choosing at least one application from a plurality of applications to process the image, and a learning unit maintaining a record of image labels and the applications presently and previously processing the images.

Abstract: A patient-specific electronic medical record may include past imaging information, as well as parameter settings, protocol identifications, and any other information extracted from the previous imaging data or derived from such data. The EMR may also include non-imaging data, such as clinical data, and results of various examinations performed of a non-imaging type. The electronic medical record may be used for a range or purposes, such as diagnosing patient conditions, recommending treatments, recognition of at-risk patients for pre-emptive and reactive care, and so forth. Moreover, the information in the EMR may be used directly for setting imaging system parameters in future imaging acquisition and post-acquisition processing.

Abstract: The acquisition, reconstruction, processing, analysis, display and visualization of imaging data in a medical diagnostic context is influenced by information stored in an electronic medical record. The electronic medical record may include past imaging information, as well as parameter settings, protocol identifications, and any other information extracted from the previous imaging data or derived from that data. The EMR may also include non-imaging data, such as clinical data, and results of various examinations performed of a non-imaging type. Based upon the information in the electronic medical record, recommendations of future imaging may be made, as well as recommendations of protocols, and techniques for acquisition, reconstruction, processing, analysis, display and visualization. The information in the EMR may be used directly for setting imaging system parameters in future imaging acquisition and post-acquisition processing.

Abstract: Certain aspects of a method and system for generating a collage of images or videos to summarize a medical dataset are disclosed. Aspects of one method may include segmenting medical images based on a type of tissue. A portion of each of the segmented medical images may be selected based on one or more selection criteria by fitting a bounding box encompassing each selected portion. A collage of images may be generated based on the selected portion of each of the segmented medical images. The generated collage of images may be stored in an image storage repository.

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.