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Motion-based segmentation of chest and abdomen region of neonates from videos
KTH, School of Electrical Engineering (EES), Signal Processing.ORCID iD: 0000-0003-1285-8947
2015 (English)In: ICAPR 2015 - 2015 8th International Conference on Advances in Pattern Recognition, 2015Conference paper, Published paper (Refereed)
Abstract [en]

Respiration rate (RR) is one of the important vital signs used for clinical monitoring of neonates in intensive care units. Due to the fragile skin of the neonates, it is preferable to have monitoring systems with minimal contact with the neonate. Recently, several methods have been proposed for contact-free monitoring of vital signs using a video camera. Detection of the chest-and-abdomen region of the neonate is crucial to determining the respiration rate accurately. We propose a technique for automatic selection of the region of interest (ROI) in neonates using motion. Our approach is based on the observation that points on the chest-and-abdomen region, and hence, the corresponding optic flow vectors, exhibit coherency in the motion caused by breathing. The motion induced due to the movement of the neonate (e.g., hands and legs) is not coherent and hence does not exhibit the characteristics of respiratory motion. We evaluate the proposed technique using several videos of neonates and demonstrate that it picks up the ROI accurately in spite of the movement of the neonate. We compare its performance with that of the standard motion history image (MHI) framework, using different metrics. Results indicate that our method can be profitably employed in RR studies.

Place, publisher, year, edition, pages
2015.
Keyword [en]
neonate monitoring, optical flow, respiration rate, vector motion history image, Video segmentation, Intensive care units, Motion analysis, Motion estimation, Optical flows, Pattern recognition, Video cameras, Automatic selection, Clinical monitoring, Motion based segmentation, Motion history images, Respiratory motions, The region of interest (ROI), Image segmentation
National Category
Medical Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-168335DOI: 10.1109/ICAPR.2015.7050663ISI: 000380489000018Scopus ID: 2-s2.0-84925664635ISBN: 9781479974580 (print)OAI: oai:DiVA.org:kth-168335DiVA: diva2:818072
Conference
8th International Conference on Advances in Pattern Recognition, ICAPR 2015, 4 January 2015 through 7 January 2015
Note

QC 20150608

Available from: 2015-06-08 Created: 2015-06-02 Last updated: 2016-10-06Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf