CO2022011610A1 - Method implemented by machine learning for the detection of blood vessels through pixel clustering - Google Patents

Method implemented by machine learning for the detection of blood vessels through pixel clustering

Info

Publication number
CO2022011610A1
CO2022011610A1 CONC2022/0011610A CO2022011610A CO2022011610A1 CO 2022011610 A1 CO2022011610 A1 CO 2022011610A1 CO 2022011610 A CO2022011610 A CO 2022011610A CO 2022011610 A1 CO2022011610 A1 CO 2022011610A1
Authority
CO
Colombia
Prior art keywords
blood vessels
detection
machine learning
method implemented
pixel clustering
Prior art date
Application number
CONC2022/0011610A
Other languages
Spanish (es)
Inventor
Jaraba Luis José Escaf
Puccini Fanny Judith Sales
González Reynaldo Villarreal
Nobles Juan Pablo Pestana
Sepulveda Paola Andrea Amar
Ramírez Jorge José Martínez
Roberto Pestana
Sales Luis Escaf
Sonia Escaf
Original Assignee
Univ Simon Bolivar
Clinica Oftalmologica Del Caribe S A S
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Univ Simon Bolivar, Clinica Oftalmologica Del Caribe S A S filed Critical Univ Simon Bolivar
Priority to CONC2022/0011610A priority Critical patent/CO2022011610A1/en
Priority to PCT/CO2023/000016 priority patent/WO2024037676A1/en
Publication of CO2022011610A1 publication Critical patent/CO2022011610A1/en

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B3/00—Apparatus for testing the eyes; Instruments for examining the eyes
    • A61B3/10—Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
    • A61B3/13—Ophthalmic microscopes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F2/00—Filters implantable into blood vessels; Prostheses, i.e. artificial substitutes or replacements for parts of the body; Appliances for connecting them with the body; Devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61F—FILTERS IMPLANTABLE INTO BLOOD VESSELS; PROSTHESES; DEVICES PROVIDING PATENCY TO, OR PREVENTING COLLAPSING OF, TUBULAR STRUCTURES OF THE BODY, e.g. STENTS; ORTHOPAEDIC, NURSING OR CONTRACEPTIVE DEVICES; FOMENTATION; TREATMENT OR PROTECTION OF EYES OR EARS; BANDAGES, DRESSINGS OR ABSORBENT PADS; FIRST-AID KITS
    • A61F9/00—Methods or devices for treatment of the eyes; Devices for putting-in contact lenses; Devices to correct squinting; Apparatus to guide the blind; Protective devices for the eyes, carried on the body or in the hand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
    • G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
    • G09B21/00—Teaching, or communicating with, the blind, deaf or mute

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Theoretical Computer Science (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Public Health (AREA)
  • Ophthalmology & Optometry (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • General Physics & Mathematics (AREA)
  • Surgery (AREA)
  • Medical Informatics (AREA)
  • Vascular Medicine (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Transplantation (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Cardiology (AREA)
  • Multimedia (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Educational Technology (AREA)
  • Eye Examination Apparatus (AREA)

Abstract

La solución pretendida, resuelve el problema planteado al aportar un método novedoso que comprende el uso de una inteligencia artificial (IA) previamente entrenada a través de un clustering de píxeles que permite identificar el tono de los mismos. Así, es capaz de segmentar y diferenciar vasos sanguíneos del resto del fondo del ojo.The intended solution solves the problem posed by providing a novel method that includes the use of artificial intelligence (AI) previously trained through pixel clustering that allows the pixel tone to be identified. Thus, it is capable of segmenting and differentiating blood vessels from the rest of the fundus of the eye.

CONC2022/0011610A 2022-08-17 2022-08-17 Method implemented by machine learning for the detection of blood vessels through pixel clustering CO2022011610A1 (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CONC2022/0011610A CO2022011610A1 (en) 2022-08-17 2022-08-17 Method implemented by machine learning for the detection of blood vessels through pixel clustering
PCT/CO2023/000016 WO2024037676A1 (en) 2022-08-17 2023-08-16 System implemented in a neural network for detecting blood vessels by means of pixel segmentation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CONC2022/0011610A CO2022011610A1 (en) 2022-08-17 2022-08-17 Method implemented by machine learning for the detection of blood vessels through pixel clustering

Publications (1)

Publication Number Publication Date
CO2022011610A1 true CO2022011610A1 (en) 2024-02-26

Family

ID=89940836

Family Applications (1)

Application Number Title Priority Date Filing Date
CONC2022/0011610A CO2022011610A1 (en) 2022-08-17 2022-08-17 Method implemented by machine learning for the detection of blood vessels through pixel clustering

Country Status (2)

Country Link
CO (1) CO2022011610A1 (en)
WO (1) WO2024037676A1 (en)

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10115194B2 (en) * 2015-04-06 2018-10-30 IDx, LLC Systems and methods for feature detection in retinal images
CN108771530B (en) * 2017-05-04 2021-03-30 深圳硅基智能科技有限公司 Fundus lesion screening system based on deep neural network
US11534064B2 (en) * 2017-06-20 2022-12-27 University Of Louisville Research Foundation, Inc. Segmentation of retinal blood vessels in optical coherence tomography angiography images
EP3924885A4 (en) * 2019-02-12 2022-11-16 National University of Singapore Retina vessel measurement
US20220160228A1 (en) * 2019-03-20 2022-05-26 Carl Zeiss Meditec Ag A patient tuned ophthalmic imaging system with single exposure multi-type imaging, improved focusing, and improved angiography image sequence display
CN111931816A (en) * 2020-07-09 2020-11-13 河南工业大学 Parallel processing method and device for retina images

Also Published As

Publication number Publication date
WO2024037676A1 (en) 2024-02-22

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