Patent application number | Description | Published |
20090016583 | System and Method for Detecting Spherical and Ellipsoidal Objects Using Cutting Planes - A method for detecting spherical and ellipsoidal objects is digitized medical images includes providing a 2-dimensional (2D) slice I(x, y) extracted from a medical image volume of a colon, said image volume comprising a plurality of intensities associated with a 3 grid of points, generating a plurality of templates of different sizes whose shape matches a target structure being sought in said slice, calculating a normalized gradient from said slice, calculating a diverging field gradient response (DFGR) for each of the plurality of masks with the normalized gradient, and selecting a strongest response as being indicative of the position and size of the target structure. | 01-15-2009 |
20090016589 | Computer-Assisted Detection of Colonic Polyps Using Convex Hull - A method for performing computer-assisted diagnosis includes receiving a plurality of two-dimensional views of an internal structure, defining a search space around one or more areas of analysis within each view of the internal structure, calculating a convex hull for each area of analysis within each search space of each view of the internal structure, determining a set of foreground pixels that are located within the convex hull for each area of analysis within each search space within each view of the internal structure, and for each area of analysis, merging the set of foreground pixels that are located within the convex hull from each view. | 01-15-2009 |
20090074272 | Method and system for polyp segmentation for 3D computed tomography colonography - A method and system for polyp segmentation in computed tomography colonogrphy (CTC) volumes is disclosed. The polyp segmentation method utilizes a three-staged probabilistic binary classification approach for automatically segmenting polyp voxels from surrounding tissue in CTC volumes. Based on an input initial polyp position, a polyp tip is detected in a CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. | 03-19-2009 |
20090080747 | User interface for polyp annotation, segmentation, and measurement in 3D computed tomography colonography - A method and system for providing a user interface for polyp annotation, segmentation, and measurement in computer tomography colonography (CTC) volumes is disclosed. The interface receives an initial polyp position in a CTC volume, and automatically segments the polyp based on the initial polyp position. In order to segment the polyp, a polyp tip is detected in the CTC volume using a trained 3D point detector. A local polar coordinate system is then fit to the colon surface in the CTC volume with the origin at the detected polyp tip. Polyp interior voxels and polyp exterior voxels are detected along each axis of the local polar coordinate system using a trained 3D box. A boundary voxel is detected on each axis of the local polar coordinate system based on the detected polyp interior voxels and polyp exterior voxels by boosted 1D curve parsing using a trained classifier. This results in a segmented polyp boundary. The segmented polyp is displayed in the user interface, and a user can modify the segmented polyp boundary using the interface. The interface can measure the size of the segmented polyp in three dimensions. The user can also use the interface for polyp annotation in CTC volumes. | 03-26-2009 |
20090092300 | System and Method for Lesion Detection Using Locally Adjustable Priors - According to an aspect of the invention, a method for training a classifier for classifying candidate regions in computer aided diagnosis of digital medical images includes providing a training set of annotated images, each image including one or more candidate regions that have been identified as suspicious, deriving a set of descriptive feature vectors, where each candidate region is associated with a feature vector. A subset of the features are conditionally dependent, and the remaining features are conditionally independent. The conditionally independent features are used to train a naïve Bayes classifier that classifies the candidate regions as lesion or non-lesion. A joint probability distribution that models the conditionally dependent features, and a prior-odds probability ratio of a candidate region being associated with a lesion are determined from the training images. A new classifier is formed from the naïve Bayes classifier, the joint probability distribution, and the prior-odds probability ratio. | 04-09-2009 |
20090092302 | System and Method for Robust Segmentation of Pulmonary Nodules of Various Densities - A method for differentiating pulmonary nodules in digitized medical images includes identifying an object of interest from a digital image of the lungs, computing a first distance map of each point of the object of interest, determining a seed point from the first distance map, starting from the seed point, growing a first region by adding successive adjacent layers of points until a background point is reached, and partitioning the first region into a nodule region and a non-nodule region. | 04-09-2009 |
20090309874 | Method for Display of Pre-Rendered Computer Aided Diagnosis Results - A method for displaying pre-rendered medical images on a workstation includes receiving three-dimensional medical image data. A region of suspicion is automatically identified within the three-dimensional medical image data. A rendering workstation is used to pre-render the three-dimensional medical image data into a sequence of two-dimensional images in which the identified region of suspicion is featured from a vantage point that is automatically selected to maximize diagnostic value of the two-dimensional images for determining whether the region of suspicion is an actual abnormality. The sequence of pre-rendered two-dimensional images is then stored in a PACS, where it can then be displayed on a viewing workstation. | 12-17-2009 |
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 |
20110064289 | Systems and Methods for Multilevel Nodule Attachment Classification in 3D CT Lung Images - Automated and semi-automated systems and methods for detection and classification of structures within 3D lung CT images using voxel-level segmentation and subvolume-level classification. | 03-17-2011 |
20110075920 | Multi-Level Contextual Learning of Data - Described herein is a framework for automatically classifying a structure in digital image data are described herein. In one implementation, a first set of features is extracted from digital image data, and used to learn a discriminative model. The discriminative model may be associated with at least one conditional probability of a class label given an image data observation Based on the conditional probability, at least one likelihood measure of the structure co-occurring with another structure in the same sub-volume of the digital image data is determined. A second set of features may then be extracted from the likelihood measure. | 03-31-2011 |
20120088981 | Matching of Regions of Interest Across Multiple Views - Described herein is a framework for multi-view matching of regions of interest in images. According to one aspect, a processor receives first and second digitized images, as well as at least one CAD finding corresponding to a detected region of interest in the first image. The processor determines at least one candidate location in the second image that matches the CAD finding in the first image. The matching is performed based on local appearance features extracted for the CAD finding and the candidate location. In accordance with another aspect, the processor receives digitized training images representative of at least first and second views of one or more regions of interest. Feature selection is performed based on the training images to select a subset of relevant local appearance features to represent instances in the first and second views. A distance metric is then learned based on the subset of local appearance features. The distance metric may be used to perform matching of the regions of interest. | 04-12-2012 |
20120172700 | Systems and Methods for Viewing and Analyzing Anatomical Structures - Systems and methods for supporting a diagnostic workflow from a computer system are disclosed herein. In accordance with one implementation, a set of pre-identified anatomical landmarks associated with one or more structures of interest within one or more medical images are presented to a user. In response to a user input selecting at least one or more regions of interest including one or more of the pre-identified anatomical landmarks, the user is automatically navigated to the selected region of interest. In another implementation, a second user input selecting one or more measurement tools is received. An evaluation may be automatically determined based on one or more of the set of anatomical landmarks in response to the second user input. | 07-05-2012 |
20140294263 | Synchronized Navigation of Medical Images - Disclosed herein is a framework for facilitating synchronized image navigation. In accordance with one aspect, at least first and second medical images are received. A non-linear mapping between the first and second medical images is generated. A selection of a given location in the first medical image is received in response to a user's navigational operation. Without deforming the second medical image, a target location in the second medical image is determined by using the non-linear mapping. The target location corresponds to the given location in the first medical image. An optimized deformation-free view of the second medical image is generated based at least in part on the target location. While the user performs navigational operations on the first medical image, the framework repeatedly receives the selection of the given location, determines the target location using the non-linear mapping, and generates the optimized deformation-free view of the second medical image based at least in part on the target location. | 10-02-2014 |