Patent application number | Description | Published |
20080260222 | Lesion Quantification and Tracking Using Multiple Modalities - A method for lesion detection includes acquiring pre-therapy medical image data from a first modality. Post-therapy medical image data is acquired from a second modality. A transformation matrix for transforming from an image space of the first modality to an image space of the second modality is calculated. A volume of interest is defined from the medical image data of the first modality. The volume of interest includes one or more lesions. The volume of interest is automatically copied to the medical image data of the second modality using the calculated transformation matrix. Treatment is directed to the lesion using the medical image data of the second modality including the copied volume of interest data. | 10-23-2008 |
20080267483 | Registration of Medical Images Using Learned-Based Matching Functions - A method for registering a medical image includes acquiring a first medical image of a subject. One or more simulated medical images are synthesized based on the acquired first medical image. One or more matching functions are trained using the first medical image and the simulated medical images. A second medical image of the subject is acquired. The first medical image and the second medical image are registered using the one or more trained matching functions. | 10-30-2008 |
20080298662 | Automatic Detection of Lymph Nodes - A method for detecting lymph nodes in a medical image includes receiving image data. One or more regions of interest are detected from within the received image data. One or more lymph node candidates are identified using a set of predefined parameters that is particular to the detected region of interest where each lymph node candidate is located. The identifying unit may identify the one or more lymph node candidates by performing DGFR processing. The method may also include receiving user-provided adjustments to the predefined parameters that are particular to the detected regions of interest and identifying the lymph node candidates based on the adjusted parameters. The lymph node candidates identified based on the adjusted parameters may be displayed along with the image data in real-time as the adjustments are provided. | 12-04-2008 |
20090034813 | Joint Detection and Localization of Multiple Anatomical Landmarks Through Learning - A method for detecting and localizing multiple anatomical landmarks in medical images including: receiving an input requesting identification of a plurality of anatomical landmarks in a medical image; applying a multi-landmark detector to the medical image to identify a plurality of candidate locations for each of the anatomical landmarks; for each of the anatomical landmarks, applying a landmark-specific detector to each of its candidate locations, wherein the landmark-specific detector assigns a score to each of the candidate locations, and wherein candidate locations having a score below a predetermined threshold are removed; applying spatial statistics to groups of the remaining candidate locations to determine, for each of the anatomical landmarks, the candidate location that most accurately identifies the anatomical landmark; and for each of the anatomical landmarks, outputting the candidate location that most accurately identifies the anatomical landmark. | 02-05-2009 |
20090037919 | Information-Theoretic View of the Scheduling Problem in Whole-Body Computer Aided Detection/Diagnosis (CAD) - A method for automatically scheduling tasks in whole-body computer aided detection/diagnosis (CAD), including: (a) receiving a plurality of tasks to be executed by a whole-body CAD system; (b) identifying a task to be executed, wherein the task to be executed has an expected information gain that is greater than that of each of the other tasks; (c) executing the task with the greatest expected information gain and removing the executed task from further analysis; and (d) repeating steps (b) and (c) for the remaining tasks. | 02-05-2009 |
20090116716 | Learning A Coarse-To-Fine Matching Pursuit For Fast Point Search In Images Or Volumetric Data Using Multi-Class Classification - A landmark location system for locating landmarks in volumes includes a medical image database including volumes of medical images, a learning unit that trains a multi-class classifier to locate a landmark point in each volume from extracted features of the volumes near a sample point offset from the landmark point and discrete displacements of the sample point to the landmark point, and a landmark locator that locates the landmark point in an input volume using the trained multi-class classifiers. | 05-07-2009 |
20090129641 | System and Method for Additive Spatial/Intensity Decomposition of Medical Images - A method for decomposing digital medical images includes providing a digital medical image, segmenting the image into one or more biological structures, extracting one or more segmented biological structures from the image by extracting all voxels within a spatial extent of each of the biological structures to construct one or more new component volumes of the biological structures. For each of the one or more new component volumes, generate a sequence of 2-dimensional projective views by moving a projection viewpoint around each the biological structure in the one or more new component images, and generate a 2-dimensional projective view from each viewpoint, and display a cine loop of the sequence of projective views where the biological structures appear to be rotating in the display. | 05-21-2009 |
20090161937 | Robust Anatomy Detection Through Local Voting And Prediction - A method for performing a medical imaging study includes acquiring a preliminary scan. A set of local feature candidates is automatically detected from the preliminary scan. The accuracy of each local feature candidate is assessed using multiple combinations of the other local feature candidates and removing a local feature candidate that is assessed to have the lowest accuracy. The assessing and removing steps are repeated until only a predetermined number of local feature candidates remain. A region of interest (ROI) is located from within the preliminary scan based on the remaining predetermined number of local feature candidates. A medical imaging study is performed based on the location of the ROI within the preliminary scan. | 06-25-2009 |
20090310836 | Automatic Learning of Image Features to Predict Disease - A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality. | 12-17-2009 |