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.
This is a preview of subscription content, log in via an institution to check access.
References
Lee, H., Rusin, C.G., Lake, D.E., et al., A new algorithm for detecting central apnea in neonates. Physiol Meas. 33:1–17, 2012.
Di Fiore, J.M., Poets, C.F., Gauda, E., et al., Cardiorespiratory events in preterm infants: Etiology and monitoring technologies. J Perinatol. 36:165–171, 2015.
Brand, D., Lau, E., Cameron, J., Wareham, R., Usher-Smith, J., Bridge, P., Lasenby, J., and Iles, R., Tidal breathing parameters measurement by structured light plethysmography (SLP) and spirometry. Am. J. Resp. Crit. Care. 181:A2528, 2010.
Janssen, R., Wang, W., Moço, A., and de Haan, G., Video-based respiration monitoring with automatic region of interest detection. Physiological Measurement. 37:100–114, 2015.
Villarroel, M., Davis, S., Watkinson, P., Guazzi, A., McCormick, K., Tarassenko, L., Jorge, J., Shenvi, A., and Green, G., Continuous non-contact vital sign monitoring in neonatal intensive care unit. Healthc Tech Lett. 1(3):87–91, 2014.
Makkapati V, Raman P and Pai G (2016) Camera based Respiration Rate of Neonates by Modelling Movement of Chest and Abdomen Region.11th International Conference on Signal Processing and Communications (SPCOM). Bangalore: IEEE:1–5
Wiesner, S., and Yaniv, Z., Monitoring patient respiration using a single optical camera 29th annual Int. Conf IEEE Eng Med Biol Soc:2740–2743, 2007.
Bartula, M., Tigges, T., and Muehlsteff, J., Camera-based system for contactless monitoring of respiration 35th annual Int. Conf IEEE Eng Med Biol Soc:2672–2675, 2013.
Eastwood-Sutherland, C., Gale, T.J., Dargavillc, P.A., and Wheeler, K., Elements of vision based respiratory monitoring. Biomed Eng Int Conf:1–5, 2015.
Shi, J., Leung, T., and Malik, J., Image and video segmentation: The normalized cut framework. Image processing, 1998. ICIP 98. Proceedings. 1998 international conference on image processing. IEEE. 1:943–947, 1998.
Shi J. (2004). MATLAB Normalized Cuts Segmentation Code. https://www.cis.upenn.edu/~jshi/software/. Accessed 7 Apr. 2017
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Meharmeet Kaur declares that she has no conflict of interest.
Andrew P. Marshall declares that he has no conflict of interest.
Caillin Eastwood-Sutherland declares that he has no conflict of interest.
Brian P. Salmon declares that he has no conflict of interest.
Peter A. Dargaville declares that he has no conflict of interest.
Timothy J. Gale declares that he has no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the University of Tasmania Human Research Ethics Committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from the parents of all individual participants included in the study.
Additional information
This article is part of the Topical Collection on Image & Signal Processing
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Kaur, M., Marshall, A.P., Eastwood-Sutherland, C. et al. Automatic Torso Detection in Images of Preterm Infants. J Med Syst 41, 134 (2017). https://doi.org/10.1007/s10916-017-0782-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10916-017-0782-8