The Charles Stark Draper Laboratory, Inc.

University of Pittsburgh

Tha Charles Stark Draper Laboratory, Inc.

Nathan Lowry

Rami S Mangoubi

Mukund Desai

Paul J Sammak

Richard Wood

John M Irvine

Peter Lommel

Lei Hamilton

Teresa M Erb

David Floyd

Stephen Duncan

Matteo Convertino

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.

System and method for classifying and quantifying age-related macular degeneration

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.

System and method for the selection of optical coherence tomography slices

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.

System and method for the segmentation of optical coherence tomography slices

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.

Detecting species diversity by image texture analysis

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.

Mathematical image analysis based cell reprogramming with applications for epigenetic and non-epigenetic base induced pluripotent stem cell derivation

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.

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Lexington, MA, United States of America

Nathan Lowry