Siemens Medical Solutions Usa, Inc.

Jianming Liang

Luca Bogoni

Jinbo Bi

Marcos Salganicoff

Matthias Wolf

Alok Gupta

Senthil Periaswamy

Pascal Cathier

Bernard S Ghanem

Zhiyun Xue

A method for computer aided detection of pulmonary emboli includes acquiring medical image data. A pulmonary embolism candidate comprising a cluster of voxels is identified. It is determined whether the candidate is a true pulmonary embolism or a false positive based on a spatial distribution of intensity values for the voxels of the cluster of voxels. The pulmonary embolism candidate is presented to a user when the candidate is determined to be a true pulmonary embolism.

Computer aided detection of pulmonary embolism with local characteristic features in CT angiography

A computer-implemented method for identifying an object of interest includes providing input data including an image and a candidate for the object of interest in the image, extracting a boundary of the candidate, and extracting a segment of a region of interest containing the candidate. The method further includes determining a plurality of features of an extracted segment of the region of interest containing the candidate, and outputting the object of interest, wherein the object of interest is characterized by the plurality of features, wherein the object of interest and the plurality of features are stored as computer-readable code.

Reducing false positives for automatic computerized detection of objects

A system for automatically detecting pulmonary emboli from medical image data includes receiving image data, automatically detecting one or more pulmonary embolism candidates from the image data, segmenting an airway tract from the image data, segmenting an artery structure from the image data, calculating a distance between each of the candidates and a nearest portion of the segmented airway, determining whether each of the candidates is within or outside of the segmented artery structure, rejecting candidates based on the calculated distance between each of the candidates and the nearest portion of the segmented airway and the determination as to whether each of the candidates is within or outside of the segmented artery structure, and indicating the location of the non-rejected candidates within the image data.

Reduction of lymph tissue false positives in pulmonary embolism detection

A method for detecting pulmonary embolisms in computed tomographic (CT) angiography includes providing a digitized (CT) image acquired from inside a pulmonary vessel, the image comprising a plurality of intensities corresponding to a 3-dimensional grid of voxels, for each voxel in the image, extracting a first pulmonary embolism (PE) candidate and PE boundary from the voxel, and for each voxel in the PE boundary, selecting a voxel from the PE boundary, extracting a subsequent PE candidate and PE boundary from the voxel, merging the subsequent PE candidate with the first PE candidate, and merging the subsequent PE boundary with the first PE boundary.

System and method for computer aided detection of pulmonary embolism in tobogganing in CT angiography

A system and method for toboggan-based object detection in cutting planes are provided. A method for detecting an object in an image includes: determining a region of interest (ROI) in the image; determining a toboggan potential for each image element in the ROI; extracting a plurality of cutting planes from the ROI; and performing a tobogganing in the cutting planes to form a toboggan cluster to determine a location of the object, wherein image elements inside the toboggan cluster are stored in a cluster-member list, image elements on an outer-border of the toboggan cluster are stored in an outer-border list and image elements on an inner-border of the toboggan cluster are stored in an inner-border list.

System and method for toboggan-based object detection in cutting planes

A method for detecting an object within a structure includes performing tobogganing on image data to obtain one or more voxel clusters and to provide a rough indication of the structure. Each of the obtained voxel clusters is characterized as an object candidate and a set of features are determined for each object candidate. Correlations between pairs of the object candidates are measured. Each of the object candidates is classified as either a true object or a non-object based on the set of features and the measured correlations.

Method of multiple instance learning and classification with correlations in object detection

A method of segmenting an object in a digital image comprising providing a digital image comprising a plurality of intensities corresponding to a domain of points in a N-dimensional space, selecting a region of interest in the image, determining a threshold intensity value for points in said region of interest, wherein an object of interest is defined by points with an intensity above a first pre-determined threshold, computing a distance map for each point in said object of interest, tobogganing each point in said object of interest based on said distance map, and selecting a cluster based on the results of said tobogganing.

System and method for toboggan-based object segmentation using distance transform

A method of identifying an object in a digital image includes finding a point in a digital image that is a concentration location, initializing a cluster with said concentration location, adding the neighboring points of the concentration location to a list, selecting a neighbor point with an extremal potential value from said list, determining a slide direction of all neighbors of said selected point and identifying those neighbors that slide to the selected point, adding those neighbor points not already in the list to the list, adding the selected point to the cluster, and repeating the steps of selecting a neighbor point with an extremal potential value, determining a slide direction, adding points to the list, and adding the selected point to the cluster, until the list is empty.

System and method for dynamic fast tobogganing

A method and device for segmenting one or more candidates in an image having image elements is disclosed. The method includes identifying a location for one of the candidates in the image, where the location is based at a given image element, and computing one or more response values at neighboring image elements that are in a neighborhood of the location. Image element clusters are created from the computed response values and one or more of the image element clusters are selected as object segmentations for one or more of the candidates.

System and method for toboggan based object segmentation using divergent gradient field response in images

A method and apparatus for characterizing an image. The method selects one or more toboggan potentials from the image, or a portion thereof, to be tobogganed. It toboggans the selected toboggan potentials to generate one or more toboggan parameters, forming at least one toboggan cluster using one or more of the toboggan parameters. It also selects one or more of the toboggan clusters to compute at least one feature parameter to characterize the image or a portion thereof.

Toboggan-based shape characterization

A system and method for detecting a shape in an image are provided. The method comprises: constructing a deformable model from an image; deforming the deformable model to remove an undesired shape in a portion of the image; computing properties of the deformed model to enable detection of a desired shape in the portion of the image; and detecting the desired shape based on the computed properties.

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Paoli, PA, United States of America

Jianming Liang