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1. US20200211235 - Method of modifying a retina fundus image for a deep learning model

Office
United States of America
Application Number 16634442
Application Date 24.07.2018
Publication Number 20200211235
Publication Date 02.07.2020
Grant Number 11200707
Grant Date 14.12.2021
Publication Kind B2
IPC
G06T 11/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
112D image generation
A61B 3/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
A61B 3/12
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
12for looking at the eye fundus, e.g. ophthalmoscopes
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
CPC
A61B 3/0025
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
0016Operational features thereof
0025characterised by electronic signal processing, e.g. eye models
G06T 11/001
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
112D [Two Dimensional] image generation
001Texturing; Colouring; Generation of texture or colour
A61B 3/12
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
12for looking at the eye fundus, e.g. ophthalmoscopes
G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
04Architectures, e.g. interconnection topology
G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computer systems based on biological models
02using neural network models
08Learning methods
G06T 7/0014
GPHYSICS
06COMPUTING; CALCULATING; COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
0002Inspection of images, e.g. flaw detection
0012Biomedical image inspection
0014using an image reference approach
Applicants National University of Singapore
Singapore Health Services Pte Ltd
Inventors Wynne Hsu
Mong Li Lee
Gilbert Lim
Tien Yin Wong
Shu Wei Daniel Ting
Agents Womble Bond Dickinson (US) LLP
John J. Penny, Jr.
Priority Data 10201706186V 28.07.2017 SG
Title
(EN) Method of modifying a retina fundus image for a deep learning model
Abstract
(EN)

A method of modifying a retina fundus image for a deep learning model is disclosed. In a described embodiment, the method includes converting a retina fundus image to a binary image by converting pixels of the retina fundus image to low intensity modified pixels and high intensity modified pixels of the binary image, and determining a first boundary between the low intensity modified pixels and high intensity modified pixels of the binary image. The method further includes removing outlier boundary values from the first boundary, constructing a second boundary from remaining boundary values, identifying the pixels of the retina fundus image that are within the second boundary, and constructing a modified retina fundus image containing the identified pixels for a deep learning model.