Color correction of baby images for cyanosis detection

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Abstract

An accurate assessment of the bluish discoloration of cyanosis in the newborn baby’s skin is essential for the doctors when making a comprehensive evaluation or a treatment decision. To date, midwives employ the score of APGAR to note any occurrence of discoloration on skin among newborn babies. However, there is still no known general method to automatically determine a cyanosis skin color and quantifying technique in a newborn baby. Furthermore, a viable yardstick is absent for evaluation purposes in training sessions. Hence, this study proposes a cyanosis skin detection in the image of a newborn with a new algorithm for a color correction using MacBeth Color Checker. This proposed system has three steps: (i) selecting cyanosis region of interest from images, (ii) correcting color via an algorithm to calibrate images, and (iii) generating a database of cyanosis CIE L*a*b* (CIELAB) values. This proposed method calculates color error with ΔE* via comparing the actual color value of MacBeth Colorchecker especially before and after applying correction for color. This proposed method to detect cyanosis allows modification of images with minimal effect upon image quality, thus assuring the viability in detecting and ascertaining values of CIELAB for cyanosis skin. Besides, this study hopes to use the outcomes of CIELAB values of cyanosis skin in order to develop a baby manikin with cyanosis that is high in fidelity in upcoming studies. This study is not associated to clinical purposes.

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 22nd Conference, Proceedings
Subtitle of host publication22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings
EditorsMark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Place of PublicationCham
PublisherSpringer
Pages354-370
Number of pages17
ISBN (Electronic)978-3-319-95921-4
ISBN (Print)978-3-319-95920-7
DOIs
Publication statusPublished - 17 Aug 2018
Event22nd Conference on Medical Image Understanding and Analysis (MIUA 2018) - University of Southampton, Southampton, United Kingdom
Duration: 9 Jul 201811 Jul 2018

Publication series

NameCommunications in Computer and Information Science
Volume894
ISSN (Print)1865-0929

Conference

Conference22nd Conference on Medical Image Understanding and Analysis (MIUA 2018)
Abbreviated titleMIUA 2018
CountryUnited Kingdom
CitySouthampton
Period9/07/1811/07/18

Fingerprint

Skin
Color
Discoloration
Comprehensive Evaluation
Region of Interest
Viability
Image Quality
Fidelity
Image quality
Calculate
Evaluation

Keywords

  • CIELAB
  • Color calibration
  • Color correction
  • Cyanosis
  • Image processing
  • Linear least square
  • Medical image
  • Medical training
  • Neonatal care

Cite this

Azmi, N. F. B., Delbressine, F., Feijs, L., & Oetomo, S. B. (2018). Color correction of baby images for cyanosis detection. In M. Nixon, S. Mahmoodi, & R. Zwiggelaar (Eds.), Medical Image Understanding and Analysis - 22nd Conference, Proceedings: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings (pp. 354-370). (Communications in Computer and Information Science; Vol. 894). Cham: Springer. https://doi.org/10.1007/978-3-319-95921-4_33
Azmi, Nur Fatihah Binti ; Delbressine, Frank ; Feijs, Loe ; Oetomo, Sidarto Bambang. / Color correction of baby images for cyanosis detection. Medical Image Understanding and Analysis - 22nd Conference, Proceedings: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings. editor / Mark Nixon ; Sasan Mahmoodi ; Reyer Zwiggelaar. Cham : Springer, 2018. pp. 354-370 (Communications in Computer and Information Science).
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abstract = "An accurate assessment of the bluish discoloration of cyanosis in the newborn baby’s skin is essential for the doctors when making a comprehensive evaluation or a treatment decision. To date, midwives employ the score of APGAR to note any occurrence of discoloration on skin among newborn babies. However, there is still no known general method to automatically determine a cyanosis skin color and quantifying technique in a newborn baby. Furthermore, a viable yardstick is absent for evaluation purposes in training sessions. Hence, this study proposes a cyanosis skin detection in the image of a newborn with a new algorithm for a color correction using MacBeth Color Checker. This proposed system has three steps: (i) selecting cyanosis region of interest from images, (ii) correcting color via an algorithm to calibrate images, and (iii) generating a database of cyanosis CIE L*a*b* (CIELAB) values. This proposed method calculates color error with ΔE* via comparing the actual color value of MacBeth Colorchecker especially before and after applying correction for color. This proposed method to detect cyanosis allows modification of images with minimal effect upon image quality, thus assuring the viability in detecting and ascertaining values of CIELAB for cyanosis skin. Besides, this study hopes to use the outcomes of CIELAB values of cyanosis skin in order to develop a baby manikin with cyanosis that is high in fidelity in upcoming studies. This study is not associated to clinical purposes.",
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Azmi, NFB, Delbressine, F, Feijs, L & Oetomo, SB 2018, Color correction of baby images for cyanosis detection. in M Nixon, S Mahmoodi & R Zwiggelaar (eds), Medical Image Understanding and Analysis - 22nd Conference, Proceedings: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings. Communications in Computer and Information Science, vol. 894, Springer, Cham, pp. 354-370, 22nd Conference on Medical Image Understanding and Analysis (MIUA 2018), Southampton, United Kingdom, 9/07/18. https://doi.org/10.1007/978-3-319-95921-4_33

Color correction of baby images for cyanosis detection. / Azmi, Nur Fatihah Binti; Delbressine, Frank; Feijs, Loe; Oetomo, Sidarto Bambang.

Medical Image Understanding and Analysis - 22nd Conference, Proceedings: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings. ed. / Mark Nixon; Sasan Mahmoodi; Reyer Zwiggelaar. Cham : Springer, 2018. p. 354-370 (Communications in Computer and Information Science; Vol. 894).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - Azmi, Nur Fatihah Binti

AU - Delbressine, Frank

AU - Feijs, Loe

AU - Oetomo, Sidarto Bambang

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N2 - An accurate assessment of the bluish discoloration of cyanosis in the newborn baby’s skin is essential for the doctors when making a comprehensive evaluation or a treatment decision. To date, midwives employ the score of APGAR to note any occurrence of discoloration on skin among newborn babies. However, there is still no known general method to automatically determine a cyanosis skin color and quantifying technique in a newborn baby. Furthermore, a viable yardstick is absent for evaluation purposes in training sessions. Hence, this study proposes a cyanosis skin detection in the image of a newborn with a new algorithm for a color correction using MacBeth Color Checker. This proposed system has three steps: (i) selecting cyanosis region of interest from images, (ii) correcting color via an algorithm to calibrate images, and (iii) generating a database of cyanosis CIE L*a*b* (CIELAB) values. This proposed method calculates color error with ΔE* via comparing the actual color value of MacBeth Colorchecker especially before and after applying correction for color. This proposed method to detect cyanosis allows modification of images with minimal effect upon image quality, thus assuring the viability in detecting and ascertaining values of CIELAB for cyanosis skin. Besides, this study hopes to use the outcomes of CIELAB values of cyanosis skin in order to develop a baby manikin with cyanosis that is high in fidelity in upcoming studies. This study is not associated to clinical purposes.

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Azmi NFB, Delbressine F, Feijs L, Oetomo SB. Color correction of baby images for cyanosis detection. In Nixon M, Mahmoodi S, Zwiggelaar R, editors, Medical Image Understanding and Analysis - 22nd Conference, Proceedings: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings. Cham: Springer. 2018. p. 354-370. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-319-95921-4_33