Eastman-kodak Company

Carestream Health, Inc.

Kodak Alaris Inc.

Nathan D Cahill

Lawrence Allen Ray

Shoupu Chen

Marvin M Goodgame

Andrew Charles Gallagher

Alexander C Loui

Edward B Gindele

John Patrick Spence

David Kloosterman

Michal Kucer

David Messinger

Kenneth Alan Parulski

Jiebo Luo

Jay S Schildkraut

Peter O Stubler

John T Keech

Joseph F Revelli, Jr

Donald O Bigelow

Sreeram Dhurjaty

Thomas F Powers

Zhaohui Sun

Gabriel Fielding

Mark R Bolin

Cranos M Williams

A system and method that performs iterative foreground detection and multi-object segmentation in an image is disclosed herein. A new background prior is introduced to improve the foreground segmentation results. Three complimentary methods detect and segment foregrounds containing multiple objects. The first method performs an iterative segmentation of the image to pull out the salient objects in the image. In a second method, a higher dimensional embedding of the image graph is used to estimate the saliency score and extract multiple salient objects. A third method uses a metric to automatically pick the number of eigenvectors to consider in an alternative method to iteratively compute the image saliency map. Experimental results show that these methods succeed in accurately extracting multiple foreground objects from an image.

Iterative method for salient foreground detection and multi-object segmentation

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 for detecting undesirable images within an image sequence, comprising the steps of receiving the image sequence; computing one or more associated image characteristics for a group of images, wherein the group of images includes at least one image from the image sequence; identifying any undesirable group of images based on the computed image characteristics; collecting undesirable groups of images into at least one collection; receiving a threshold value of a minimum number of undesirable images; and flagging as undesirable only images that belong to one or more collections that have at least the threshold value of undesirable images.

Method and system for detection of undesirable images

A system for the detection of abnormalities in a medical image of a subject. The system includes an examination bundle, a learning engine, and a detecting engine. The examination bundle includes at least one medical image of the subject from a first modality and at least one medical image of the subject from a second modality. The learning engine is employed for determining the characteristics of abnormalities within the at least one medical image from the first modality and the at least one medical image from the second modality. The detecting engine is employed for detecting abnormalities within at least one of the medical images comprising the examination bundle.

Abnormality detection in medical images

An image indexer for indexing a plurality of images that includes a first data structure for subsequent classification of the one or more images. The first data structure includes characteristics for at least one class. An image classifier classifies one or more individual images found in the plurality of images as classified images according to the first data structure. A second data structure performs subsequent clustering of the plurality of images, wherein the second data structure includes at least two sequential events in a set of known events. The classified images are clustered according to the second data structure, and a representative image is selected from each cluster of classified images.

Image indexer for indexing a plurality of images

A system for identifying anatomical structure depicted in an in vivo image, that includes an examination bundlette having a captured in vivo image; and a gastrointestinal atlas that includes a list of individual anatomical structures and characterization data of the individual anatomical structures. A classification engine analyzes the examination bundlette and the gastrointestinal atlas to identify the anatomical structure depicted in the captured in vivo image.

System and method for classifying in vivo images according to anatomical structure

A method for delivering radiation therapy to a patient using a three-dimensional planning image for radiation therapy of the patient wherein the planning image includes a radiation therapy target. The method includes the steps of: determining desired image capture conditions for the capture of at least one two-dimensional radiographic image of the radiation therapy target using the three-dimensional planning image; detecting a position of the radiation therapy target in the at least one captured two-dimensional radiographic image; and determining a delivery of the radiation therapy in response to the radiation therapy target's detected position in the at least one captured two-dimensional radiographic image.

Radiation therapy method with target detection

A digital image processing method for multiple passes diagnostic alignment of in vivo images, comprising the steps of: acquiring images using an in vivo video camera system; forming an in vivo video camera system examination bundlette; transmitting the examination bundlette to proximal in vitro computing device(s); processing the transmitted examination bundlette; automatically identifying abnormalities in the transmitted examination bundlette; and setting off alarm signals to a local site provided that suspected abnormalities have been identified for each pass forming a registration bundle; selecting identification elements of an image from the registration bundle of one pass; and retrieving corresponding images from another pass.

Method and system for multiple passes diagnostic alignment for in vivo images

A digital image processing method for image registration, that acquires a reference intensity image and a floating intensity image that is to be registered; and preprocesses the reference and the floating images, before converting the preprocessed reference image to a vectorized reference image. Subsequently, the vectorized reference image is converted to a reference index image. Additional image processing includes spatially transforming the preprocessed floating image using a transformation matrix; converting the transformed floating image to a vectorized floating image; converting the vectorized floating image to a floating index image; and obtaining joint statistics of the index images. Other steps include, computing a cost function due to misalignment of the two images using the joint statistics; and updating the transformation matrix and repeating several aforementioned steps, if the cost function does not satisfy a predefined criterion, otherwise, applying the transformation matrix to an acquired floating intensity image.

Method of image registration using mutual information

A method for detecting a geometrically transformed copy of content in at least a portion of an image, comprises the steps of: (a) providing first and second digital images; (b) searching for objects of interest within each digital image; (c) identifying pairs of corresponding objects of interest in the digital images, wherein each pair of corresponding objects of interest comprises a located object of interest in the first digital image and a corresponding located object of interest in the second digital image that corresponds to the located object of interest in the first image; (d) locating feature points on each located object of interest in each digital image; (e) matching feature points on the located object of interest in the first digital image to the feature points on the corresponding object of interest in the second digital image, thereby generating a set of correspondence points for each image; (f) determining parameters of a geometric transformation that maps the set of correspondence points in the first digital image into the set of correspondence points in the second digital image; (g) transforming the first digital image according to the parameters of the geometric transformation determined in step (f); and (h) detecting regions of similarity between the content of the transformed first digital image and the second digital image, thereby determining if the second image contains a region that is a geometrically transformed copy of a region in the first image.

Method and system for detecting a geometrically transformed copy of an image

An electronic imaging system for capturing an image of a scene includes an optical system for producing an optical image of the scene, an imaging sensor having a surface in optical communication with the optical system, and a plurality of imaging elements distributed on the surface of the imaging sensor according to a distribution representable by a nonlinear function in which the relative density of the distributed imaging elements is greater toward the center of the sensor. Such a distribution provides physical coordinates for the imaging elements corresponding to a projection of the scene onto a non-planar surface, thereby compensating for perspective distortion of the scene onto the non-planar surface and alleviating the need to perform geometric warping of the images after they have been captured.

Electronic imaging system having a sensor for correcting perspective projection distortion

Deriving a three-dimensional panorama from a plurality of images of a scene generated by a range imaging camera of the type that produces ambiguities in range information includes the steps of: (a) acquiring a plurality of adjacent images of the scene, wherein there is an overlap region between the adjacent images and at least some of the adjacent images are range images; (b) providing offset data for the range images in order to recover corrected relative scene spatial information and provide a corrected range image, and (c) deriving a three-dimensional panorama from the corrected range image. In order to provide offset data, a relative range difference is detected between adjacent range images as a constant offset between the adjacent images; and the constant offset is applied to at least one of adjacent range images to correct for ambiguities in the relative ranges of the range images.

Three dimensional spatial panorama formation with a range imaging system

A method for producing a cropped digital image, includes the steps of: providing a plurality of partially overlapping source digital images; providing a cropping aspect ratio L:H, the cropping aspect ratio being the ratio of the length to the height of the cropped digital image; providing a cropping criterion, the cropping criterion being a criterion for the size and location of the cropped digital image; combining the source digital images to form a composite digital image; selecting the cropping region of the composite digital image according to the cropping criterion, said cropping region being a rectangular region having aspect ratio L:H, and having size and location determined by the cropping criterion; and, cropping the composite digital image to the cropping region to form a cropped digital image.

Method and system for compositing images to produce a cropped image

A method of locating a linear defect on a photographic element, the element having a useful imaging width and the defect aligned with length of the element, includes the steps of: exposing a region of the element to create a latent image which is substantially uniform across the useful imaging width of the element; developing the latent image to produce a density signal; sampling the density signal with a photometric device; and analyzing the sampled density data for the presence of significant deviations aligned with the length of the element to locate the defect.

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West Henrietta, NY, United States of America

Nathan D Cahill