Color correction of baby images for cyanosis detection

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

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.

TaalEngels
TitelMedical Image Understanding and Analysis - 22nd Conference, Proceedings
Subtitel22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings
RedacteurenMark Nixon, Sasan Mahmoodi, Reyer Zwiggelaar
Plaats van productieCham
UitgeverijSpringer
Pagina's354-370
Aantal pagina's17
ISBN van elektronische versie978-3-319-95921-4
ISBN van geprinte versie978-3-319-95920-7
DOI's
StatusGepubliceerd - 17 aug 2018
Evenement22nd Conference on Medical Image Understanding and Analysis (MIUA 2018) - University of Southampton, Southampton, Verenigd Koninkrijk
Duur: 9 jul 201811 jul 2018

Publicatie series

NaamCommunications in Computer and Information Science
Volume894
ISSN van geprinte versie1865-0929

Congres

Congres22nd Conference on Medical Image Understanding and Analysis (MIUA 2018)
Verkorte titelMIUA 2018
LandVerenigd Koninkrijk
StadSouthampton
Periode9/07/1811/07/18

Vingerafdruk

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

Trefwoorden

    Citeer dit

    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 (editors), Medical Image Understanding and Analysis - 22nd Conference, Proceedings: 22nd Conference, MIUA 2018, Southampton, UK, July 9-11, 2018, Proceedings (blz. 354-370). (Communications in Computer and Information Science; Vol. 894). Cham: Springer. DOI: 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. redacteur / Mark Nixon ; Sasan Mahmoodi ; Reyer Zwiggelaar. Cham : Springer, 2018. blz. 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’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",
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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 (redactie), 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, blz. 354-370, Southampton, Verenigd Koninkrijk, 9/07/18. DOI: 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. redactie / Mark Nixon; Sasan Mahmoodi; Reyer Zwiggelaar. Cham : Springer, 2018. blz. 354-370 (Communications in Computer and Information Science; Vol. 894).

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer 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

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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.

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