Patent application number | Description | Published |
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 |
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 |
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 |
20100034440 | Anatomical Primitive Detection - A method of detecting an anatomical primitive in an image volume includes detecting a plurality of transformationally invariant points (TIPS) in the volume, aligning the volume using the TIPs, detecting a plurality landmark points in the aligned volume that are indicative of a given anatomical object, and fitting a target geometric primitive as the anatomical primitive based using the detected landmark points. | 02-11-2010 |
20100232686 | HIERARCHICAL DEFORMABLE MODEL FOR IMAGE SEGMENTATION - Described herein is a technology for facilitating deformable model-based segmentation of image data. In one implementation, the technology includes receiving training image data ( | 09-16-2010 |
20100284590 | Systems and Methods for Robust Learning Based Annotation of Medical Radiographs - Systems and methods for performing a medical imaging study include 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. | 11-11-2010 |
20110044534 | HIERARCHICAL CLASSIFIER FOR DATA CLASSIFICATION - Described herein is a framework for constructing a hierarchical classifier for facilitating classification of digitized data. In one implementation, a divergence measure of a node of the hierarchical classifier is determined. Data at the node is divided into at least two child nodes based on a splitting criterion to form at least a portion of the hierarchical classifier. The splitting criterion is selected based on the divergence measure. If the divergence measure is less than a predetermined threshold value, the splitting criterion comprises a divergence-based splitting criterion which maximizes subsequent divergence after a split. Otherwise, the splitting criterion comprises an information-based splitting criterion which seeks to minimize subsequent misclassification error after the split. | 02-24-2011 |
20110058720 | Systems and Methods for Automatic Vertebra Edge Detection, Segmentation and Identification in 3D Imaging - Systems and methods for automatic accurate and efficient segmentation and identification of one or more vertebra in digital medical images using a coarse-to-fine segmentation. | 03-10-2011 |