Abstract: A method for segmenting a digitized image includes providing a digitized volumetric image comprising a plurality of intensities corresponding to a domain of points in an N-dimensional space, identifying a target structure in said image, forming a window about said target structure whose size is a function of the target scale, and performing a joint space-intensity-likelihood ratio test at each point within said window to determine whether each said point is within said target structure.

Abstract: A system and method for loading a timepoint for comparison with a previously loaded timepoint are provided. The method comprises: selecting an image dataset of the timepoint; validating the image dataset of the timepoint against a validated image dataset of the previously loaded timepoint; and constructing a volume based on the image dataset of the timepoint.

Abstract: A system and method for monitoring disease progression or response to therapy using multi-modal visualization are provided. The method comprises: selecting a first image dataset of a first timepoint; loading the first image dataset of the first timepoint; selecting a second image dataset of a second timepoint; loading the second image dataset of the second timepoint; registering the first image dataset of the first timepoint and the second image dataset of the second timepoint; and displaying the first image dataset of the first timepoint and the second image dataset of the second timepoint.

Abstract: A system and method for prioritized transmission of scalable compressed data are provided, the system including a database server for receiving an interactive prioritization request from a client and prioritizing transmission of the compressed data relative to a bin optimization in response to the interactive prioritization request; and the method including receiving an interactive prioritization request from a client, prioritizing transmission of the compressed data relative to the bin optimization in response to the interactive prioritization request and transmitting the prioritized compressed data to the client.

Abstract: A system and method for linking volumes of interest (VOIs) across timepoints are provided. The method comprises: loading an image dataset of a first timepoint and an image dataset of a second timepoint; registering the image dataset of the first timepoint and the image dataset of the second timepoint; displaying the image dataset of the first timepoint and the image dataset of the second timepoint; selecting a VOI in the image dataset of the first timepoint and the image dataset of the second timepoint; and linking the VOIs in the image dataset of the first timepoint and the image dataset of the second timepoint.

Abstract: A method for three-dimensional segmentation of a target in multislice images of volumetric data includes determining a center and a spread of the target by a parametric fitting of the volumetric data, and determining a three-dimensional volume by non-parametric segmentation of the volumetric data iteratively refining the center and spread of the target in the volumetric data.

Abstract: The present invention provides a system and method for selecting prospective patients for a clinical trial. In various embodiments, a clinical trials brokerage is configured to receive requests from drug companies for lists of persons meeting specified criteria for clinical trials. Patient records are retrieved from a structured computerized patient record (CPR) data warehouse populated with comprehensive patient information mined from unstructured hospital records. A list of persons for whom consent was obtained can be outputted and forwarded to the entity interested in performing the clinical trial and which requested the list. Anonymity of a patient can be maintained until the patient provides consent to participate in the clinical trial.

Abstract: A method for determining a structure in volumetric data includes determining an anisotropic scale-space for a local region around a given spatial local maximum, determining L-normalized scale-space derivatives in the anisotropic scale-space, and determining the presence of noise in the volumetric data and upon determining noise in the volumetric data, determining the structure by a most-stable-over-scales determination, and upon determining noise below a desirable level, determining the structure by one of the most-stable-over-scales determination and a maximum-over-scales determination.

Abstract: CAD (computer-aided decision) support systems, methods and tools are provided for automated decision support for screening, evaluating, and/or diagnosing medial conditions. For example, CAD support systems and tools implement methods for automatically processing patient data for a subject patient using various interpretation methods, and integrally rending and presenting the interpretation results to a user (e.g., physician, radiologist, etc.) in a manner that enables fast and efficient screening, evaluation, and/or diagnosis of potential medical conditions of the subject patient.

Abstract: A method for determining a volume of interest in data includes determining fixed-bandwidth estimations of a plurality of analysis bandwidths, wherein the estimation of the fixed-bandwidth comprises, providing an estimate of a mode location of the volume of interest in the data, and determining a covariance of the volume of interest using a local Hessian matrix. The method further includes determining the volume of interest as a most stable fixed-bandwidth estimation across each of the plurality of analysis bandwidths.

Abstract: We propose using different classifiers based on the spatial location of the object. The intuitive idea behind this approach is that several classifiers may learn local concepts better than a “universal” classifier that covers the whole feature space. The use of local classifiers ensures that the objects of a particular class have a higher degree of resemblance within that particular class. The use of local classifiers also results in memory, storage and performance improvements, especially when the classifier is kernel-based. As used herein, the term “kernel-based classifier” refers to a classifier where a mapping function (i.e., the kernel) has been used to map the original training data to a higher dimensional space where the classification task may be easier.

Abstract: CAD (computer-aided decision) support systems, methods and tools for medical imaging are provided, which use machine learning classification for automated detection and marking of regions of interest in medical images. Machine learning methods are used for adapting/optimizing a CAD process by seamlessly incorporating physician knowledge into the CAD process using training data that is obtained during routine use of the CAD system.

Abstract: Systems and methods are provided for automatic 3D segmentation of abnormal anatomical structures such as colonic polyps, aneurisms or lung nodules, etc., in 3D medical imaging applications. For example, systems and methods for 3D lesion segmentation implement a centroid-based coordinate transformation (e.g., spherical transformation, ellipsoidal transformation, etc.) to transform a 3D surface of the lesion from an original volume space into, e.g., a spherical or ellipsoidal coordinate space, followed by interpolation of the transformed lesion surface to enable accurate determination of a boundary between a lesion and surrounding normal structures.

Abstract: CAD (computer-aided detection) systems, methods and tools are provided for automatically inserting “false” marks (e.g., incorrect marks, misleading marks, etc.) in medical images to ensure an unbiased CAD-assisted review of the marked medical images by physicians, clinicians, radiologists, etc. For example, a method for automatic detection of medical conditions in medical images includes the steps of receiving image data, processing the image data to detect potential medical conditions in the image data, adding a mark in the image data that indicates a detected medical condition, adding a false mark in the image data; and outputting marked image data comprising one or more marks that indicate a detected medical condition, or one or more false marks, or both. The individual performing a CAD-assisted review of the “marked” image data is aware that one or more “false” marks may be included in displayed images, which prevents blind reliance on the CAD results.

Abstract: The present invention provides techniques for combining various types of diagnostic images. In one embodiment of the present invention, a composite image is obtained by fusing MPR and MIP images. The MPR is obtained from a CT scan, and the MIP is obtained from a PET scan. The resulting superimposed images help a physician to see the diagnostic information in context. In other embodiments of the present invention, techniques for an MIP-MIP overlay, VR-MIP overlay, and VR-MPR overlay are provided.

Abstract: A three-dimensional (3D) imaging system and method include a processor for generating a volume-rendered 3D image on a display using a plurality of voxels from a 3D image dataset; and a view selector which responds to user inputs for determining a first set of voxels corresponding to a boundary in the volume-rendered 3D image using a predetermined boundary-specifying criteria applied to a function of the opacity of the plurality of voxels. The processor responds to the first set and to a user function selection by performing the selected function to modify the volume-rendered 3D image relative to the detected boundaries. The selected function is a display function, a measurement function, or a segmentation function. The view selector determines the boundary from voxels having opacities greater than a predetermined threshold, or determines the boundary from voxels having a gradient in opacities greater than a predetermined threshold.

Abstract: Various attributes for medical CAD analysis used alone or in combination are disclosed, including: (1) using medical sensor data and context information associated with the configuration of the medical system to provide recommendations for further acts to be performed for more accurate or different diagnosis, (2) recommending further acts to be performed and providing an indication of the importance of the further act for further diagnosis, (3) distributing the computer-assisted diagnosis software and/or database over multiple systems, and (4) transmitting patient data to a remote system for CAD diagnosis and using the results of the CAD diagnosis at the remote facility.

Abstract: A system and method for assigning feature sensitivity values to a set of potential measurements to be taken during a medical procedure of a patient in order to provide a medical diagnosis is disclosed. Data is received from a sensor that represents a particular medical measurement. The received data and context data are analyzed with respect to one or more sets of training models. Feature sensitivity values are derived for the particular medical measurement and other potential measurements to be taken based the analysis, and the feature sensitivity values are outputted.

Abstract: The present invention provides techniques for combining various types of diagnostic images. In one embodiment of the present invention, a composite image is obtained by fusing MPR and MIP images. The MPR is obtained from a CT scan, and the MIP is obtained from a PET scan. The resulting superimposed images help a physician to see the diagnostic information in context. In other embodiments of the present invention, techniques for an MIP-MIP overlay, VR-MIP overlay, and VR-MPR overlay are provided.

Abstract: A system and method for data compression and visualization includes a system having a compression unit for compressing an input image of voxels in accordance with a weighted visualization importance, and a visualization unit in signal communication with the compression unit for visualizing the voxels in an order corresponding to the weighted visualization importance; and a corresponding method for defining a weighting function responsive to a visualization importance parameter for voxels, deriving an order of transmission for the voxels in correspondence with the weighting function, compressing the voxels with a look-up table indicative of the order of transmission, transmitting the compressed voxels in order of decreasing weighted visualization importance, and where the method is optionally for receiving the compressed voxels in order of decreasing weighted visualization importance, decompressing the voxels with the look-up table indicative of the order of transmission, and visualizing a voxel in the order i