Eastman-kodak Company

Carestream Health, Inc.

Other

Omnivision Technologies, Inc.

Intellectual Ventures Fund 83 LLC

Monument Peak Ventures, LLC

Edward B Gindele

Andrew Charles Gallagher

Lawrence Allen Ray

Richard Alan Simon

Kevin Edward Spaulding

Jiebo Luo

James E Adams, Jr

Navid Serrano

Raymond William Ptucha

Kenneth Alan Parulski

David C Smart

Joel S Lawther

Anthony Dirisio

John F Hamilton, Jr

Bruce Harold Pillman

John D Buhr

Nathan D Cahill

Amy D Enge

Robert T Gray

Andreas E Savakis

Jeffrey Clarence Snyder

Stephen P Etz

Majid Rabbani

Jay S Schildkraut

John A Weldy

Rajiv Mehrotra

Michael D Heath

Karin Topfer

Geoffrey John Woolfe

Ann L McCarthy

Robert J Parada

Henry Nicponski

Wei Zhu

William Vernon Fintel

Michael S Axman

Keath T Chen

Gabriel Fielding

David K Rhoda

David A Hodder

Stephen E Johnson

James R Niederbaumer

Robert J Wilson

Michael W Dupin

Robert Luke Walker

Alex Lopez-Estrada

A method for multi-dimensional image processing obtains a multi-dimensional image having a plurality of data elements, each data element having a corresponding data value. The method forms a reduced noise image by repeated iterations of a process that adjusts the data value for one or more data elements p of the obtained image by: for each data element k in a group of data elements in the image, calculating a weighting factor for the data element k as a function of the difference between an estimated data value for data element p and the corresponding data value of data element k, updating the estimated data value for the data element p according to the combined calculated weighting factors and the data value of the data element k; and displaying, storing, or transmitting the reduced noise image.

CBCT image processing method

An apparatus captures radiographic images of an animal standing proximate the apparatus. A moveable x-ray source and a digital radiographic detector are hidden from view of the animal and are revolved about a portion of the animal's body to capture one or a sequence of radiographic images of the animal's body.

Extremity imaging for animals

Methods and/or apparatus for iteratively reconstructing a 3-dimensional volume for an object captured with radiographic imaging that includes an electronic sensor, exemplary methods can be performed at least in part on a computer, can include receiving a 3-dimensional volume data set generated from imaging an object; identifying a set of intra system components corresponding to system components used to capture images of the object, where the intra system components include at least an x-ray source within system components configured to be placed before the object; representing x-ray emission characteristics of the x-ray source as a function of angle and energy; using the 3D emission characteristics to simulate the forward projection of x-ray photons to generate a primary image and/or a scattered image; and using the primary image and/or the scattered image to reconstruct an improved 3-dimensional volume data set.

Monte carlo modeling of field angle-dependent spectra for radiographic imaging systems

A method for generating a 3-dimensional reconstruction model of an object of interest that lies within a volume, the method executed at least in part by a computer, acquires a first set of projection images of the volume at a first exposure and a first field of view and a second set of projection images of the object of interest within the volume at a second exposure that is higher than the first exposure and a second field of view that is narrower than the first field of view. An object of interest is reconstructed from the second set of projection images according to information related to portions of the volume that lie outside the object of interest. The reconstructed object of interest is displayed.

Reconstruction of a cone beam scanned object

A method for providing a calculated volume data value for an organ of a patient acquires image data for a first volume image series and for at least a second volume image series for the patient and displays the first volume image series. The method responds to an instruction to generate the volume data value by identifying a seed point in the first volume image series, correlating the seed point to the second volume image series, segmenting the organ in at least the second volume image series according to the correlated seed point, and calculating the volume data value for the organ using at least the segmented second volume image series. The calculated volume data value can be displayed.

Reporting organ volume for a medical digital image

A method for obtaining a segmented region corresponding to an anatomical organ in a volume image generates a smoothed image from the volume image and forms a core segmentation of the volume image according to the smoothed image. The size of the core segmentation of the volume image is increased to form the segmented region for the anatomical organ according to data obtained from the core segmentation. Data corresponding to the segmented region is stored in a memory.

Segmenting an organ in a medical digital image

A method of collecting information regarding an anatomical object of interest includes displaying an image characterized by a first region and a second region, wherein the first and second regions are mutually exclusive and the object is displayed within the second region, selecting first and second points spanning the object in the displayed image, at least one of the points being within the first region, and extracting a plurality of statistical values from image voxels, lying on a line segment between the first and second points, that correspond to the object.

Information collection for segmentation of an anatomical object of interest

A method of bounding an anatomical object of interest in a 3-dimensional volume image includes displaying an image of at least a portion of the object, selecting a plurality of points in the displayed image, at least a first and second point of the plurality of points spanning the object, forming a non-rectilinear surface bounding the plurality of points, identifying a seed point within the surface and extracting a plurality of statistical values corresponding to image voxels disposed proximate the seed point, and classifying image voxels within the surface into a first class and a second class based on the plurality of statistical values.

Method for determining a bounding surface for segmentation of an anatomical object of interest

A method and a system are disclosed for labeling an anatomical point associated with a lesion in an organ such as a lung. The method includes: a segmentation of a vessel tree anatomical structure starting from an autonomously determined initial image point; labeling the vessel segments of the vessel tree segmentation with segment labels based on a priori anatomical knowledge, thereby creating an individualized anatomical model; receiving a user-specified image point having a location from a user and locating a nearby vessel structure; tracking along the vessel structure in a direction towards a root of a parent vessel tree until a prior labeled vessel segment is encountered in the anatomical model, and assigning the label of the encountered prior labeled vessel segment from the anatomical model as an anatomical location label of the user-specified image point.

Method for lung lesion location identification

A method of image segmentation includes receiving a set of voxels, segmenting the set of voxels into a foreground group and a background group, and classifying voxels of the foreground group as either lesion voxels or normal anatomy voxels. The method also includes blocking the normal anatomy voxels and performing a second segmentation on voxels of the background group and the lesion voxels, the second segmentation forming a stage two foreground group comprising the lesion voxels and a portion of the voxels of the background group. The method further includes classifying voxels of the stage two foreground group as either stage two lesion voxels or stage two normal anatomy voxels.

Method for segmentation of lesions

A digital image editing method includes receiving a three-dimensional volume image of an anatomical object of interest, wherein the volume image is characterized by first and second mutually exclusive segmentation classes. The method also includes deriving a two-dimensional slice image from the volume image, selecting a single point on the slice image within the second segmentation class, and defining a plane in response to the selection of the single point, the plane dividing the second segmentation class into a target portion corresponding to the object and a remainder portion.

Method for editing 3D image segmentation maps

A method for producing a distance map of scene distance values for a digital image captured by a digital camera includes capturing a first digital image of a scene under a first illumination condition, wherein the first digital image includes a plurality of pixels and the scene includes a plurality of scene objects located at different distances from the digital camera, capturing a second digital image of the scene under a second illumination condition that is different from the first illumination condition, and using the first and second digital images to produce a distance map having a plurality of scene distance values, wherein each scene distance value relates to the distance between the digital camera and the corresponding scene object.

Determining scene distance in digital camera images

A method of adjusting exposure of a digital camera based on range information, including a digital camera capturing a first digital image at a selected exposure of a scene having objects; providing range information having two or more range values indicating the distance from the digital camera to objects in the scene; using the range information and pixel values of the captured digital image to determine an exposure adjustment amount for the selected exposure; and applying the exposure adjustment amount to the digital image to produce a second digital image with adjusted exposure.

Adjusting digital image exposure and tone scale

A method of analyzing a lesion in a medical digital image using at least one point contained within a lesion to be analyzed includes propagating a wave-front surface from the point(s) for a plurality of steps; partitioning the wave-front surface into a plurality of wave-front parts wherein each wave-front part is associated with a different portion of the wave-front surface corresponding to a previous propagation step; and analyzing at least one feature associated with each wave-front part to classify anatomical structures associated with the lesion and normal anatomy within the medical digital image.

Analyzing lesions in a medical digital image

A method for processing a source digital image wherein the source digital image is comprised of a plurality of pixels. A spatial filter is applied to the source digital image to produce an enhanced digital image. An inverse spatial filter is applied to the enhanced digital image to produce an estimated digital image. A difference digital image is then produced from the estimated digital image and the source digital image, wherein the difference digital image is representative of a difference between the source digital image and the estimated digital image. The difference digital image and the enhanced digital image can be transmitted from a first device to a second device remote. At the second device, a reconstructed digital image can be generated from the difference digital image and the enhanced digital image, wherein the reconstructed digital image is substantially equivalent to the source digital image.

Digital image reconstruction using inverse spatial filtering

Growing community of inventors

Rochester, NY, United States of America

Edward B Gindele