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Abstract

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.

Item Type:	Article
Authors/Creators:	Kaur, M and Marshall, AP and Eastwood-Sutherland, C and Salmon, BP and Dargaville, PA and Gale, TJ
Keywords:	image processing, NICU, non-contact respiratory monitoring, paediatrics, respiratory rate, torso detection
Journal or Publication Title:	Journal of Medical Systems
Publisher:	Kluwer Academic/Plenum Publ
ISSN:	0148-5598
DOI / ID Number:	10.1007/s10916-017-0782-8
Copyright Information:	Copyright 2017 Springer Science+Business Media, LLC
Related URLs:	Google Scholar: Salmon, BP UTAS Profile: Salmon, BP UTAS Profile: Dargaville, PA Google Scholar: Gale, TJ UTAS Profile: Gale, TJ
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