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US 2018/0005417 A1Published application
System and method for forming a super-resolution biomarker map image
AssigneeCubisme, Inc.
InventorMoira F. SCHIEKE
Life of this patent
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Abstract
A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.
Claims20
A method comprising:
- receiving, by an image computing unit, image data from a sample, wherein the image data corresponds to one or more image datasets, and wherein each of the image datasets comprises a plurality of images;
- receiving selection, by the image computing unit, of at least two image datasets from the one or more image datasets having the image data;
- creating, by the image computing unit, three-dimensional (3D) matrices from each of the at least two image datasets that are selected;
- refining, by the image computing unit, the 3D matrices;
- applying, by the image computing unit, one or more matrix operations to the refined 3D matrices;
- receiving, by the image computing unit, selection of matrix column from the 3D matrices;
- applying, by the image computing unit, a convolution algorithm to the selected matrix column for creating a two-dimensional (2D) matrix;
- and
- applying, by the image computing unit, a reconstruction algorithm to create a super-resolution biomarker map (SRBM) image.
6 more elements
A reconstruction method comprising:
- generating, by an image computing unit, a two-dimensional (2D) matrix that corresponds to probability density functions for a biomarker;
- identifying, by the image computing unit, a first color scale for a first moving window;
- computing, by the image computing unit, a mixture probability density function for each voxel of a super resolution biomarker map (SRBM) image based on first moving window readings of the first moving window from the 2D matrix;
- determining, by the image computing unit, a first complementary color scale for the mixture probability density function of each voxel;
- identifying, by the image computing unit, a maximum a posterior (MAP) value based on the mixture probability density function;
- and
- generating, by the image computing unit, the SRBM image based on the MAP value of each voxel using the first complementary color scale.
4 more elements
An image computing system, comprising:
- a database configured to store image data;
- and
- an image computing unit configured to:
- retrieve the image data the database, wherein the image data corresponds to one or more image datasets, and wherein each of the image datasets comprises a plurality of images;
- receive selection of at least two image datasets from the one or more image datasets having the image data;
- create three-dimensional (3D) matrices from each of the at least two image datasets that are selected;
- refine the 3D matrices;
- apply one or more matrix operations to the refined 3D matrices;
- receive selection of matrix column from the 3D matrices;
- apply a convolution algorithm to the selected matrix column for creating a two-dimensional (2D) matrix;
- and
- apply a reconstruction algorithm to create a super-resolution biomarker map (SRBM) image.
9 more elements
Dependencies are inferred from the claim text.