Abstract: An area of Barrett's oesophagus in a subject's oesophagus is quantified from a video image signal representing a video image of the subject's oesophagus captured using a camera of an endoscope. Depth estimation on the frames to derive depth maps in respect of frames of the video image. Regions of the frames corresponding to an area of Barrett's oesophagus in the subject's oesophagus are segmented. A value of a geometrical measure of the area of Barrett's oesophagus is calculated using the depth map and segmented region in respect of at least one of the frames.

Abstract: A method for characterising motion of one or more objects in a time ordered image dataset comprising a plurality of time ordered data frames, the method comprising: selecting a reference data frame from the plurality of time ordered data frames (210); extracting a plurality of image patches from at least a part of the reference data frame (220); identifying a location of each image patch of at least a subset of the plurality of image patches in each data frame (230); defining, based on the identified locations, a mesh for each data frame, wherein vertices of each mesh correspond to respective identified locations of image patches in the corresponding data frame (240); and deriving, from the meshes, a motion signature for the time ordered image dataset, the motion signature characteristic of the motion of the one or more objects in the plurality of time ordered data frames (250).

Abstract: Methods and systems for generating visual representations of variation of disease-relevant classification are disclosed. Training data is received that comprises sample data units from subjects that represent information about a biological sample via an N-dimensional set of values. A dimensionality reduction algorithm represents each sample data unit as a respective point in a reduced dimension parameter space. Distributions of points from the dimensionality reduction are used to derive a probability density distribution for each of a plurality of disease-relevant classifications in the reduced dimension parameter space. A visual representation of each of the derived probability density distributions in the reduced dimension parameter space is generated to provide a visual representation of disease-relevant classification variation over the parameter space.

Abstract: An analysis apparatus analyses a video image signal comprising successive frames of imaging an endoscopy procedure. A machine learning block analyses the video image signal using a machine learning technique that classifies regions of the frames as belonging to one of plural classes corresponding to respective types of image artefact. The classes include a motion blur class corresponding to motion blur of the image, at least one erroneous exposure class corresponding to a type of erroneous exposure of the image, and at least one noise artefact class corresponding to a type of image artefact that is noise. A quality score block derives quality scores representing image quality of the successive frames based on the classified regions.

Abstract: A method for characterising motion of one or more objects in a time ordered image dataset comprising a plurality of time ordered data frames, the method comprising: selecting a reference data frame from the plurality of time ordered data frames (210); extracting a plurality of image patches from at least a part of the reference data frame (220); identifying a location of each image patch of at least a subset of the plurality of image patches in each data frame (230); defining, based on the identified locations, a mesh for each data frame, wherein vertices of each mesh correspond to respective identified locations of image patches in the corresponding data frame (240); and deriving, from the meshes, a motion signature for the time ordered image dataset, the motion signature characteristic of the motion of the one or more objects in the plurality of time ordered data frames (250).

Abstract: A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.

Abstract: Embodiments of the disclosure are directed to segmenting a digital image of biological tissue into biological units, such as cells. A first weak or data driven segmentation is generated using image data representing the digital image to segment the digital image into a first set of biological units. Applying a geometric model, each unit in the first set of biological units is ranked based on a similarity in shape and scale between the unit and one or more other units in the image. A subset of units from the first set of biological units is selected based on the rank of each biological unit relative to a predetermined threshold rank. A second weak or data driven segmentation may then be generated using image data including the subset of biological units to segment that portion of the digital image into a second set of biological units.

Abstract: A method for image based inspection of an object includes receiving an image of an object from an image capture device, wherein the image includes a representation of the object with mil-level precision. The method further includes projecting a measurement feature of the object from the image onto a three-dimensional (3D) model of the object based on a final projection matrix; determining a difference between the projected measurement feature and an existing measurement feature on the 3D model; and sending a notification including the difference between the projected measurement feature and the existing measurement feature.

Abstract: An approach for seismic data analysis is provided. In accordance with various embodiments, the active learning approaches are employed in conjunction with an analysis algorithm that is used to process the seismic data. Algorithms that may employ such active learning include, but are not limited to, ranking algorithms and classification algorithms.

Abstract: In accordance with one aspect of the present technique, a method is disclosed. The method includes receiving a new video from one or more sensors and generating a new content graph (CG) based on the new video. The method also includes comparing the new CG with a plurality of prior CGs. The method further includes identifying a first portion of the new CG matching a portion of a first prior CG and a second portion of the new CG matching a portion of the second prior CG. The method further includes analyzing a first set of semantic annotations (SAs) associated with the portion of the first prior CG and a second set of SAs associated with the portion of the second prior CG. The method further includes generating a sequence of SAs for the new video based on the analysis of the first and the second set of SAs.

Abstract: A method for image based inspection of an object includes receiving an image of an object from an image capture device, wherein the image includes a representation of the object with mil-level precision. The method further includes projecting a measurement feature of the object from the image onto a three-dimensional (3D) model of the object based on a final projection matrix; determining a difference between the projected measurement feature and an existing measurement feature on the 3D model; and sending a notification including the difference between the projected measurement feature and the existing measurement feature.

Abstract: The subject matter disclosed herein relates to X-ray imaging systems, and more specifically to digital X-ray imaging systems. In one embodiment, an imaging system includes an X-ray source configured to emit X-rays. The imaging system also includes an X-ray detector configured to detect the emitted X-rays and produce a corresponding electrical signal. The imaging system also includes a gantry configured to at least partially revolve the X-ray source and the X-ray detector about a primary rotational axis. The X-ray detector is coupled to the gantry so that a diagonal of the X-ray detector is oriented substantially perpendicular to the primary rotational axis.

Abstract: Exemplary embodiments include methods, systems, and devices for enabling users to provide quality scores for indicating the quality of image analysis methods performed on images of biological tissue. An exemplary user interface displays results of an image analysis method performed on an image of biological tissue in an overlaid manner on an image of biological tissue. The exemplary user interface enable a user to provide, directly on the user interface, one or more quality scores to indicate the user's assessment of the quality of the image analysis performed on the image. Exemplary embodiments store the quality scores provided by the user in association with the image analysis method and the image of biological tissue.

Abstract: Improved systems and methods for the analysis of digital images are provided. More particularly, the present disclosure provides for improved systems and methods for the analysis of digital images of biological tissue samples. Exemplary embodiments provide for: i) segmenting, ii) grouping, and iii) quantifying molecular protein profiles of individual cells in terms of sub cellular compartments (nuclei, membrane, and cytoplasm). The systems and methods of the present disclosure advantageously perform tissue segmentation at the sub-cellular level to facilitate analyzing, grouping and quantifying protein expression profiles of tissue in tissue sections globally and/or locally. Performing local-global tissue analysis and protein quantification advantageously enables correlation of spatial and molecular configuration of cells with molecular information of different types of cancer.

Abstract: Methods and systems for digitally enhancing an initial image of a material to which a plurality of stains were previously applied, that generally comprise: unmixing the image into a plurality of individual reconstructed images, each individual image corresponding to one of the stains; estimating a residual image corresponding to the difference between the original image and the reconstructed images; adjusting one or more components of the individual images; mixing the adjusted components using one or more estimated mixing coefficients; and adding the residual image to the mixed adjusted components to generate an enhanced image.

Abstract: A method of segmenting a digital image of biological tissue includes accessing a ranking model calculated from training data representing shapes of conforming and non-conforming biological unit exemplars. The ranking model may include support vectors defining a hyperplane in a vector space. The method further includes accessing image data representing the digital image, identifying a first shape and a set of second constituent shapes in the digital image, wherein the first shape comprises a union of the set of second constituent shapes, determining a rank of a first data point in the image data corresponding to the first shape and a rank of a second data point in the image data corresponding to the set of second constituent shapes into the vector space, and segmenting the digital image using the first shape or the set of second constituent shapes based on which data point has a greater respective rank.

Abstract: Improved systems and methods for the analysis of digital images are provided. More particularly, the present disclosure provides for improved systems and methods for the analysis of digital images of biological tissue samples. Exemplary embodiments provide for: i) segmenting, ii) grouping, and iii) quantifying molecular protein profiles of individual cells in terms of sub cellular compartments (nuclei, membrane, and cytoplasm). The systems and methods of the present disclosure advantageously perform tissue segmentation at the sub-cellular level to facilitate analyzing, grouping and quantifying protein expression profiles of tissue in tissue sections globally and/or locally. Performing local-global tissue analysis and protein quantification advantageously enables correlation of spatial and molecular configuration of cells with molecular information of different types of cancer.

Abstract: A workflow is presented that facilitates defining geocontextual information as a set of rules for multiple seismic attributes. Modeling algorithms may be employed that facilitate analysis of multiple seismic attributes to find candidate regions that are most likely to satisfy the set of rules. These candidates may then be sorted based on how well they represent the geocontextual information.

Abstract: An approach for seismic data analysis is provided. In accordance with various embodiments, the active learning approaches are employed in conjunction with an analysis algorithm that is used to process the seismic data. Algorithms that may employ such active learning include, but are not limited to, ranking algorithms and classification algorithms.

Abstract: Systems and methods for segmenting images comprising cells, wherein the images comprise a plurality of pixels; one or more three dimensional (3D) clusters of cells are identified in the images; and the 3D clusters of cells are automatically segmented into individual cells using one or more models.