Semi-automated machine learning approach to segment and register tissue oxygenation maps onto clinical images of wounds Conference

Robledo, Edwin, Schutzman, Richard, Fang, Ruogu et al. (2019). Semi-automated machine learning approach to segment and register tissue oxygenation maps onto clinical images of wounds . SMART BIOMEDICAL AND PHYSIOLOGICAL SENSOR TECHNOLOGY XI, 10873 10.1117/12.2510065

cited authors

  • Robledo, Edwin; Schutzman, Richard; Fang, Ruogu; Fernandez, Cristianne; Kwasinski, Rebecca; Leiva, Kevin; Perez-Clavijo, Francisco; Godavarty, Anuradha

sustainable development goals

authors

date/time interval

  • February 5, 2019 -

publication date

  • January 1, 2019

webpage

published in

keywords

  • Image analysis
  • Life Sciences & Biomedicine
  • Optics
  • Physical Sciences
  • Physics
  • Physics, Applied
  • Radiology, Nuclear Medicine & Medical Imaging
  • Science & Technology
  • co-registration
  • image segmentation
  • infrared spectroscopy
  • medical and biological imaging
  • wounds

Location

  • San Francisco, CA

Digital Object Identifier (DOI)

International Standard Book Number (ISBN) 13

Conference

  • Conference on Optical Biopsy XVII - Toward Real-Time Spectroscopic Imaging and Diagnosis

publisher

  • SPIE-INT SOC OPTICAL ENGINEERING

volume

  • 10873