David S Freund

US Patent:

20110242306, Oct 6, 2011

Filed:

Dec 22, 2009

Appl. No.:

13/139499

Inventors:

Neil Bressler - Owings Mill MD, US
Philippe Martin Burlina - North Bethesda MD, US
David Eric Freund - Columbia MD, US

Assignee:

THE JOHNS HOPKINS UNIVERSITY - BALTIMORE MD

International Classification:

G06K 9/00
H04N 7/18

US Classification:

348 78, 382128, 348E07085

Abstract:

A system and method for automated detection of age related macular degeneration and other retinal abnormalities which may have a retinal scanner capable of obtaining retinal data from a subject. The retinal scanner may be coupled to a central processing unit (CPU) which may have memory storing CPU-executable instructions which may detect retinal abnormalities. When the CPU receives retinal data from the retinal scanner, the system may perform CPU-executable instructions for detecting retinal abnormalities. The system may analyze the retinal data to determine one or more healthy areas and, based on the analyzed healthy area, the system may detect abnormalities in the retinal data.

US Patent:

20150265144, Sep 24, 2015

Filed:

Jul 10, 2013

Appl. No.:

14/437777

Inventors:

- Baltimore MD, US
David E. Freund - Columbia MD, US
Srihara Kankanahalli - Columbia MD, US
Neil M. Bressler - Owings Mills MD, US

International Classification:

A61B 3/00
A61B 3/14
A61B 5/00
A61B 3/10
G06T 7/00
A61B 3/12

Abstract:

A method of detecting, and classifying severity of, a retinal disease using retinal images includes at least one of receiving, retrieving or generating reference data that includes information concerning occurrences of key image features for each of a plurality of retinal disease and disease severity conditions; receiving a retinal image of an individual; processing the retinal image of the individual to identify occurrences of each of a plurality of distinguishable image features throughout at least a region of interest of the retinal image; identifying which ones of the identified occurrences of the plurality of distinguishable image features of the retinal image of the individual correspond to the key image features of the reference data; calculating, based on the identifying, a number of occurrences of each of the key image features in the retinal image of the individual; and determining at least one of a likelihood of a presence of a retinal disease or a likelihood of developing a retinal disease based on a comparison of the number of occurrences of each of the key image features in the retinal image of the individual to the reference data.