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Automatic torso detection in images of preterm infants

Imaging systems have applications in patient respiratory monitoring but with limited application in neonatal intensive care units (NICU). In this paper we propose an algorithm to automatically detect the torso in an image of a preterm infant during non-invasive respiratory monitoring. The algorithm uses normalised cut to segment each image into clusters, followed by two fuzzy inference systems to detect the nappy and torso. Our dataset comprised overhead images of 16 preterm infants in a NICU, with uncontrolled illumination, and encompassing variations in poses, presence of medical equipment and clutter in the background. The algorithm successfully identified the torso region for 15 of the 16 images, with a high agreement between the detected torso and the torso identified by clinical experts.

History

Publication title

Journal of Medical Systems

Volume

41

Issue

9

Article number

134

Number

134

ISSN

0148-5598

Department/School

School of Engineering

Publisher

Kluwer Academic/Plenum Publ

Place of publication

233 Spring St, New York, USA, Ny, 10013

Rights statement

Copyright 2017 Springer Science+Business Media, LLC

Repository Status

  • Restricted

Socio-economic Objectives

Neonatal and child health

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