Color correction of baby images for cyanosis detection

Nur Fatihah Binti Azmi; Frank Delbressine; Loe Feijs; Sidarto Bambang Oetomo

doi:10.1007/978-3-319-95921-4_33

Color correction of baby images for cyanosis detection

Nur Fatihah Binti Azmi, Frank Delbressine, Loe Feijs, Sidarto Bambang Oetomo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

2 Downloads (Pure)

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 language	English
Title of host publication	Medical Image Understanding and Analysis - 22nd Conference, Proceedings
Subtitle of host publication	22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings
Editors	Mark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Place of Publication	Cham
Publisher	Springer
Pages	354-370
Number of pages	17
ISBN (Electronic)	978-3-319-95921-4
ISBN (Print)	978-3-319-95920-7
DOIs	https://doi.org/10.1007/978-3-319-95921-4_33
Publication status	Published - 17 Aug 2018
Event	22nd Conference on Medical Image Understanding and Analysis (MIUA 2018) - University of Southampton, Southampton, United Kingdom Duration: 9 Jul 2018 → 11 Jul 2018

Publication series

Name	Communications in Computer and Information Science
Volume	894
ISSN (Print)	1865-0929

Conference

Conference	22nd Conference on Medical Image Understanding and Analysis (MIUA 2018)
Abbreviated title	MIUA 2018
Country/Territory	United Kingdom
City	Southampton
Period	9/07/18 → 11/07/18

Funding

Acknowledgements. The authors would like to express their gratitude and special acknowledgements to Universiti Teknikal Malaysia Melaka (UTeM) and Ministry of Higher Education, Malaysia (KPT) for the funding of the Ph.D. program of Nur Fatihah Azmi and also to the Department of Neonatology Máxima Medical Center, Veld-hoven, The Netherlands.

Keywords

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

Access to Document

10.1007/978-3-319-95921-4_33

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). Springer. https://doi.org/10.1007/978-3-319-95921-4_33

Azmi, Nur Fatihah Binti ; Delbressine, Frank ; Feijs, Loe et al. / 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).

@inproceedings{59e792139a5e4e4bab38759971577c27,

title = "Color correction of baby images for cyanosis detection",

abstract = "An accurate assessment of the bluish discoloration of cyanosis in the newborn baby{\textquoteright}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.",

keywords = "CIELAB, Color calibration, Color correction, Cyanosis, Image processing, Linear least square, Medical image, Medical training, Neonatal care",

author = "Azmi, {Nur Fatihah Binti} and Frank Delbressine and Loe Feijs and Oetomo, {Sidarto Bambang}",

year = "2018",

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doi = "10.1007/978-3-319-95921-4_33",

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booktitle = "Medical Image Understanding and Analysis - 22nd Conference, Proceedings",

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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 et al.
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 proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Color correction of baby images for cyanosis detection

AU - Azmi, Nur Fatihah Binti

AU - Delbressine, Frank

AU - Feijs, Loe

AU - Oetomo, Sidarto Bambang

PY - 2018/8/17

Y1 - 2018/8/17

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.

AB - 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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KW - Color calibration

KW - Color correction

KW - Cyanosis

KW - Image processing

KW - Linear least square

KW - Medical image

KW - Medical training

KW - Neonatal care

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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). doi: 10.1007/978-3-319-95921-4_33

Color correction of baby images for cyanosis detection

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

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Conceptual determination and assessment of cyanosis

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