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AI Biotech/Diagnostics: Cardio
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Patent US9008391
Issued 2015-04-14
Systems And Methods For Processing Retinal Images For Screening Of Diseases Or Abnormalities
Embodiments disclose systems and methods that aid in screening, diagnosis and/or monitoring of medical conditions. The design includes a system and method for accessing retinal images related to a patient, each retinal image including a plurality of pixels. For each of the retinal images, the system designates a first set of the plurality of pixels as active pixels including interesting retinal image regions and computes a first vector of numbers comprising pixel-level descriptors for each of the active pixels. The system computes a second vector of numbers for each of the retinal images and provides a second classification using supervised learning.
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- 1. A computing system comprising:
one or more hardware computer processors; and one or more storage devices configured to store software instructions configured for execution by the one or more hardware computer processors in order to cause the computing system to:
access retinal images related to a patient, each of the retinal images comprising a plurality of pixels;
for each of the retinal images, designate a first set of the plurality of pixels as active pixels including interesting retinal image regions;
for each of the retinal images, compute a first vector of numbers comprising pixel-level descriptors for each of the active pixels;
for each of the retinal images, provide a first classification for each of the active pixels using supervised learning utilizing the corresponding first vector of numbers;
for each of the retinal images, compute a second vector of numbers including one or more of:
a first histogram of pixel-level classifier decision statistics computed during the first classification;
a third vector of numbers computed by obtaining a nearest codeword of pixel-level descriptors aggregated as a second histogram of codeword frequencies in each of the retinal images, wherein a set of codewords is computed from a representative set of images; and
a third histogram of active region-level decision statistic numbers computed by: grouping neighboring pixels detected in the first set of the plurality of pixels into active regions, computing a number representing each of the active regions using one or more of: mean, median, maximum, or minimum of pixel-level classifier decision statistics computed in the first classification for each pixel that is part of at least one active region, wherein the third histogram is computed over the active regions detected in each of the retinal images; and
provide a second classification for one or more of: the plurality of pixels, an interesting region within one of the retinal images, one of the retinal images in its entirety, or a collection of the retinal images, using supervised learning.
- 10. A computer implemented method comprising:
accessing retinal images related to a patient, each of the retinal images comprising a plurality of pixels; for each of the retinal images, designating a first set of the plurality of pixels as active pixels including interesting retinal image regions; for each of the retinal images, computing a first vector of numbers comprising pixel-level descriptors for each of the active pixels; for each of the retinal images, providing a first classification for each of the active pixels using supervised learning utilizing the corresponding first vector of numbers; for each of the retinal images, computing a second vector of numbers including one or more of:
a first histogram of pixel-level classifier decision statistics computed during the first classification;
a third vector of numbers computed by obtaining a nearest codeword of pixel-level descriptors aggregated as a second histogram of codeword frequencies in each of the retinal images, wherein a set of codewords is computed from a representative set of images; and
a third histogram of active region-level decision statistic numbers computed by: grouping neighboring pixels detected in the first set of the plurality of pixels into active regions, computing a number representing each of the active regions using one or more of: mean, median, maximum, or minimum of pixel-level classifier decision statistics computed in the first classification for each pixel that is part of at least one active region, wherein the third histogram is computed over the active regions detected in each of the retinal images; and
providing a second classification for one or more of: the plurality of pixels, an interesting region within one of the retinal images, one of the retinal images in its entirety, or a collection of the retinal images, using supervised learning.
- 19. Non-transitory computer storage that stores executable program instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising:
accessing retinal images related to a patient, each of the retinal images comprising a plurality of pixels; for each of the retinal images, designating a first set of the plurality of pixels as active pixels including interesting retinal image regions; for each of the retinal images, computing a first vector of numbers comprising pixel-level descriptors for each of the active pixels; for each of the retinal images, computing a second vector of numbers including one or more of:
a first histogram of pixel-level classifier decision statistics computed during the first classification;
a third vector of numbers computed by obtaining a nearest codeword of pixel-level descriptors aggregated as a second histogram of codeword frequencies in each of the retinal images, wherein a set of codewords is computed from a representative set of images; and
a third histogram of active region-level decision statistic numbers computed by: grouping neighboring pixels detected in the first set of the plurality of pixels into active regions, computing a number representing each of the active regions using one or more of: mean, median, maximum, or minimum of pixel-level classifier decision statistics computed in the first classification for each pixel that is part of at least one active region, wherein the third histogram is computed over the active regions detected in each of the retinal images; and
providing a second classification for one or more of: the plurality of pixels, an interesting region within one of the retinal images, one of the retinal images in its entirety, or a collection of the retinal images using supervised learning.
