Abstract: Systems and methods for improved vision diagnostics are disclosed. Some embodiments relate to machine learning models for the analysis of vision diagnostics data. Some embodiments relate to improved, robust regression methods that can be used with vision diagnostics data to detect trends that may warrant medical intervention.

Abstract: The present disclosure describes a system and method to classify optical coherence tomography (OCT) images. The present system can classify OCT images without first segmenting the retina tissue. The system can generate one or more profiles from vertical transects through the OCT images. The system can identify image statistics based on the one or more profiles. The system's classifier can then classify the OCT images based on the identified image statistics.

Abstract: The present disclosure describes systems and methods to select fovea containing optical coherence tomography (OCT) images. The systems and methods described herein receive a plurality of OCT images. The portion of the OCT images are selected for further processing, where a line tracing the border between the retina and non-retina tissue is generated. A difference of the line is generated. Candidate OCT images are then generated responsive to the generated difference line. The lowest point among each difference lines generated for each of the OCT images is identified, and the OCT image to which the lowest point corresponds is identified as the fovea containing OCT image.

Abstract: A method and a system for implementing a mathematical algorithm in a neural architecture and transferring that neural architecture to an integrated circuit (IC) chip. The neural architecture has neurons that are capable of converting current to frequency, voltage to frequency, frequency to frequency and time to frequency. The neurons can have multi-sensor inputs (multiple synapses) for either scaling or inhibiting neuron outputs. The neural architecture-to-hardware conversion method is specifically tailored for neural architectures for image processing applications.

Abstract: The present disclosure describes a system and method to segment optical coherence tomography (OCT) images. The present system uses a hybrid method that employs both Bayesian level sets (BLS) and graph-based segmentation algorithms. The system first identifies retinal tissue within an OCT image using the BLS algorithms. The identified retinal tissue is then further segmented using the graph-based segmentation algorithms.

Abstract: A method of measuring species diversity is provided. The method includes receiving a first image of a landscape and receiving a second image of a second landscape. The method also includes representing a portion of the first image as a first region of interest comprising a multiplicity of pixels and representing a portion of the second image as a second region of interest comprising a multiplicity of pixels. The method further includes comparing at least one textural feature of the first region of interest and the second region of interest and calculating the species diversity between the first landscape and the second landscape based on the comparison of the at least one textural features of the regions of interest.

Abstract: The present disclosure describes a system and method to classify optical coherence tomography (OCT) images. The present system can classify OCT images without first segmenting the retina tissue. The system can generate one or more profiles from vertical transects through the OCT images. The system can identify image statistics based on the one or more profiles. The system's classifier can then classify the OCT images based on the identified image statistics.

Abstract: The present disclosure describes a system and method to segment optical coherence tomography (OCT) images. The present system uses a hybrid method that employs both Bayesian level sets (BLS) and graph-based segmentation algorithms. The system first identifies retinal tissue within an OCT image using the BLS algorithms. The identified retinal tissue is then further segmented using the graph-based segmentation algorithms.

Abstract: The present disclosure describes systems and methods to select fovea containing optical coherence tomography (OCT) images. The systems and methods described herein receive a plurality of OCT images. The portion of the OCT images are selected for further processing, where a line tracing the border between the retina and non-retina tissue is generated. A difference of the line is generated. Candidate OCT images are then generated responsive to the generated difference line. The lowest point among each difference lines generated for each of the OCT images is identified, and the OCT image to which the lowest point corresponds is identified as the fovea containing OCT image.

Abstract: A method of measuring species diversity is provided. The method includes receiving a first image of a landscape and receiving a second image of a second landscape. The method also includes representing a portion of the first image as a first region of interest comprising a multiplicity of pixels and representing a portion of the second image as a second region of interest comprising a multiplicity of pixels. The method further includes comparing at least one textural feature of the first region of interest and the second region of interest and calculating the species diversity between the first landscape and the second landscape based on the comparison of the at least one textural features of the regions of interest.

Abstract: Mathematical and statistical image analysis methods and systems are applied to enhance and refine the process of reprogramming cells, for example, to modify cells from patients into custom-matched stem cells.

Abstract: The invention provides methods for determining the differentiation state of cells. The methods include non-invasive, non-perturbing, automatable, and quantitative methods of analysis of cell colonies, individual cells, and/or cellular structures.

Abstract: The invention provides methods for determining the differentiation state of cells. The methods include non-invasive, non-perturbing, automatable, and quantitative methods of analysis of cell colonies, individual cells, and/or cellular structures.

Abstract: Mathematical and statistical image analysis methods and systems are applied to enhance and refine the process of reprogramming cells, for example, to modify cells from patients into custom-matched stem cells.

Abstract: The invention provides methods for determining the differentiation state of cells. The methods include non-invasive, non-perturbing, automatable, and quantitative methods of analysis of cell colonies, individual cells, and/or cellular structures.

Abstract: The invention provides methods for determining the differentiation state of cells. The methods include non-invasive, non-perturbing, automatable, and quantitative methods of analysis of cell colonies, individual cells, and/or cellular structures.