Abstract: A system and method for tree-model visualization for detecting an abnormality in an anatomical tree structure are provided. The method comprises: fitting a tree-model to an anatomical tree structure; converting branches of the tree-model into first two-dimensional branch images; and arranging the first two-dimensional branch images in a hierarchical order to form a second two-dimensional image.

Abstract: Disclosed is an automated technique for analyzing the affected region due to an embolism in an organ. A segmented image of the organ vasculature is generated using image volume data received, for example, from a Computed Tomography (CT) machine. An embolus is then identified (either manually or automatically) within the segmented image, and the volume of the organ affected by the embolism is automatically determined. The volume of the organ affected by the embolism may be determined by computing a sub-tree within the segmented image, where the sub-tree comprises vessels that are distal to the identified embolus point. In one embodiment, the sub-tree is generated by determining a plane perpendicular to a vessel at the embolus point such that the sub-tree comprises a distal portion of the vasculature with respect to the plane. Unwanted overlapping trees are identified (e.g., by analyzing branch angles) and removed from the sub-tree.

Abstract: A method for estimating regions of detected candidates identified in a response image of an object created from a scanned Computerized Tomography image is disclosed. The regions are used to generate features. Voxels proximate to a detection center of the object are sampled. A determination is made as to which of the sampled voxels include a region surrounding the detection center. Features that characterize the region are collected.

Abstract: A method to automatically align magnetic resonance (MR) brain scans for diagnostic scan planning, including: acquiring a three-dimensional (3D) localizer image of a patient; selecting a two-dimensional (2D) coronal view and a 2D transverse view from the localizer image; identifying a mid-sagittal plane (MSP) line in each of the coronal and transverse views and calculating a 3D MSP based on the MSP lines; reconstructing the localizer image based on an equation for the 3D MSP to obtain an image of the MSP of the patient's brain; identifying crista galli (CG) and tip of the occipital bone (TOB) in the image of the MSP of the patient's brain; calculating a transformation matrix based on the MSP, CG and TOB in the image and using the transformation matrix to obtain a scan plan for the patient; and outputting the scan plan for the patient.

Abstract: A three dimensional medical image filter computes a value at a given location of the image based upon the properties of a given 3D region. The filter is defined by equations that are functions of the gradient and image values of neighboring locations. The equations determine the final value at the given location. The specific definitions of these equations determine the filter properties and may be adjusted for different applications.

Abstract: A system and method for visualizing a tree structure in medical image data is provided. The method comprises: segmenting the tree structure in the image data (230-265); coloring an exterior of the tree structure using data associated with interior components of the tree structure (270-290); and outputting an image of the tree structure colored by the interior components of the tree structure (295).

Abstract: A method for detecting a protrusion in a medical image includes: acquiring a medical image, wherein the medical image is of an anatomical part; segmenting the medical image; calculating a distance map of the medical image; calculating a gradient of the distance mapped medical image; and processing the gradient to detect a protrusion in the medical image. The gradient is processed by: projecting a plurality of rays from a location in the distance mapped medical image; calculating a value for each of the plurality of rays based on features of each of the plurality of rays and the gradient of the distance mapped medical image; summing and scaling the value of each of the plurality of rays; and detecting one of a sphere-like and polyp-like shape using the summed and scaled values of the plurality of rays, wherein one of the sphere-like and polyp-like shapes is the protrusion.

Abstract: A system and method for determining a location and a direction for viewing a protrusion, comprising: casting a plurality of rays in an outward direction from a point, wherein the point is inside a protrusion; selecting at least one of the plurality of rays for determining a location and a direction for viewing the protrusion; and determining the location and the direction for viewing the protrusion using the selected at least one of the plurality of rays.

Abstract: A method for labeling connected tubular objects within segmented image data, including: receiving segmented image data; and labeling the segmented image data to identify a plurality of components in the segmented image data, wherein the labeling includes: processing the segmented image data to create a processed image that represents centerline and radii estimates of the connected tubular components; determining seed point candidates in the processed image that are within a band of radii; grouping the candidates based on their physical distance from each other and their radii estimates; partitioning the segmented image data in accordance with the grouped candidates; and assigning a separate color label to each of the plurality of components that are different from each other.

Abstract: A computer-assisted diagnosis method for assisting diagnosis of anatomical structures in a digital volumetric medical image of at least one lung includes identifying an anatomical structure of interest in the volumetric digital medical image. The anatomical structure of interest is automatically segmented, in real-time, in a predefined volume of interest (VOI). Quantitative measurements of the anatomical structure of interest are automatically computed, real-time. A result of the segmenting step and a result of the computing step are displayed, in real-time. A likelihood that the anatomical structure of interest corresponds to a disease or an area warranting further investigation is estimating, in real-time, based on predefined criteria and the quantitative measurements. A warning is generated, in real-time, when the likelihood is above a predefined threshold.

Abstract: A mark-free computer-assisted diagnosis method and system are provided for assisting diagnosis of abnormalities in digital medical images using diagnosis based image enhancement. The method includes the steps of: receiving indicia identifying one or more regions of interest in a digital medical image; and displaying one or more enhanced views of the regions of interest, the enhanced views being based on diagnostic parameters for the regions of interest and diagnostic parameters corresponding to a particular abnormality.

Abstract: A computer assisted diagnosis method using change detection of salient features in a first and a second digital image includes the step of converting the first and the second digital image into a first and a second relational attribute graph, respectively. Each graph comprises nodes and arcs. Each node corresponds to an identified salient feature and associated with information comprising a type and characteristics of the corresponding identified salient feature. The arcs correspond to a topological arrangement of the identified salient features. An optimal inexact structural match is determined between the nodes of the first graph and the nodes of the second graph so as to form matched sets of nodes comprising one node from each graph. A node lacking a determined match is matched with a null node. The characteristics of each of the nodes in a matched set are compared to one another to identify variances among the compared characteristics.

Abstract: A method for automatically detecting lung nodules from MSHR CT images includes defining a volume of interest (VOI) for a lung volume in an MSHR CT image. The lung volume is examined using the VOI, including, determining a local histogram of intensity and adaptive threshold values for segmenting the VOI to obtain seeds. Each seed is examined to detect lung nodules therefrom, including segmenting anatomical structures represented by the seed by applying a segmentation method that adaptively adjusts a segmentation threshold value based on histogram analysis of the seed to extract the structures based on three-dimensional connectivity and histogram intensity information, and classifying each structure as a lung nodule or a non-nodule based on a priori knowledge corresponding to lung nodules and related structures. The lung nodules are displayed. The lung nodules are analyzed, including automatically quantifying lung nodule features to provide an automatic detection decision.

Abstract: A computer-assisted diagnosis method for assisting diagnosis of anatomical structures in a digital volumetric medical image of at least one lung includes identifying an anatomical structure of interest in the volumetric digital medical image. The anatomical structure of interest is automatically segmented, in real-time, in a predefined volume of interest (VOI). Quantitative measurements of the anatomical structure of interest are automatically computed, real-time. A result of the segmenting step and a result of the computing step are displayed, in real-time. A likelihood that the anatomical structure of interest corresponds to a disease or an area warranting further investigation is estimating, in real-time, based on predefined criteria and the quantitative measurements. A warning is generated, in realtime, when the likelihood is above a predefined threshold.

Abstract: A computer-assisted diagnosis method and system are provided for automatically determining diagnostic saliency of digital images. The method includes the step of providing filters for evaluating the image. Each filter is designed to identify a specific type of diagnostic finding, and is associated with the following: a virtual window for defining regions in the image at which the filter is applied; a set of training image patches corresponding to typical appearances of the specific type of diagnostic finding; a distance measure between the training image patches and the regions in the image defined by the virtual window; and a feature set corresponding to the distance measure. The filters are applied to the image to compute distances between the regions in the image defined by the virtual window and the training image patches based on the distance measure and the feature set, for each of the plurality of filters.

Abstract: A reflective rotund lens is positioned for projecting a panoramic picture of its horizontal surroundings at a given elevation onto a CCD array, for converting the picture into “pixel form” or an image signal. The image signal is digitized via an A/D converter circuit. A digital signal processor system is programmed for extracting a strip of pixel locations representative of an image ring mapped to pixel locations via superimposition upon the image, whereby all pixel locations outside of the image ring are eliminated by passing the pixels thereof through said A/D converter circuit at a rate higher than the conversion rate of said A/D converter circuit.

Abstract: A system for automatically composing target objects from a sequence of x-ray images for the purpose of diagnostic examination or measurement detects and matches semantically meaningful visual event hierarchy. The system can automatically composite the target object explicitly ignoring image regions that are not of interest for diagnosis. Diagnostic examination and measurement are helped significantly by the use of compound x-ray images. Prior to compound image generation, each constituent image undergoes two automatic pre-processing steps through a background detector and an emphasis field extractor. The system further includes a pair-wise aligner, a compound image generator and an examiner/measurer. The system increases the efficiency of compound image generation and improves its accuracy by de-emphasizing alignment of irrelevant structures in the images.

Abstract: A system for automatically tracking the location of a vehicle includes a visual image detector mounted on the vehicle for producing as the vehicle moves along a route digitized strips of image data representing successive panoramic views of scenery about the vehicle at respective locations along the route. A sparse tracking subsystem processes and stores only selected ones of the image data strips representing substantially spaced apart successive locations along the route, for use as a sparse database. A dense tracking subsystem processes and stores as a dense database every successive one of the image data strips representing location along the route, whereby the dense tracking subsystem provides more accurate location of the vehicle when it retraces some portion of the route than the sparse tracking subsystem.

Abstract: A reflective rotund lens is positioned for projecting a panoramic picture of its horizontal surroundings at a given elevation onto a CCD array, for converting the picture into "pixel form" or an image signal. The image signal is digitized via an A/D converter circuit. A digital signal processor system is programmed for extracting a strip of pixel locations representative of an image ring mapped to pixel locations via superimposition upon the image, whereby all pixel locations outside of the image ring are eliminated by passing the pixels thereof through said A/D converter circuit at a rate higher than the conversion rate of the A/D converter circuit.

Abstract: A visual gyroscope system for detecting turns and straight line travel of a vehicle includes a visual image detector mounted on the vehicle, for producing sequential and successive digitized image data strips containing information identifying scenery about the vehicle at associated successive locations along the route travelled, A processor is programmed to extract from image detector each successive pair of image data strips, and rotate the second occurring strip of each pair until it matches the first occurring strip to obtain the incremental rotation and direction of rotation of the vehicle in its travel between strips. The incremental rotations between strips are accumulated and summed to obtain the total rotation upon completion of a turn, and along with the direction of rotation, identification of a right turn, left turn, u-turn, or straight line travel.