Medical & Biological Engineering & Computing

, Volume 56, Issue 12, pp 2245–2258 | Cite as

Classification of pressure ulcer tissues with 3D convolutional neural network

  • Begoña García-Zapirain
  • Mohammed Elmogy
  • Ayman El-Baz
  • Adel S. Elmaghraby
Original Article


A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising.

Graphical Abstract

The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network.


Pressure ulcer 3D convolution neural network (CNN) Tissue classification Linear combinations of discrete Gaussians (LCDG) 



The authors thank Prof. Dr. Georgy Gimel’farb, Department of Computer Science, University of Auckland, Auckland, New Zealand, for his help in revising the paper. In addition, the authors want to thank Sofia Zahia, Connor Burns, and Daniel Sierra-Sosa for their support in summarizing the related work and preparing the masks for the GT images.

Funding information

The grants that have contributed with partial funding of the study are IT − 905 − 16 to eVIDA research group from the Basque Government, JC2015 − 00305 Josè Castillejo Research Stay Grant from the Spanish Ministry, and ACM2017_09 from the University of Deusto.


  1. 1.
    Mukherjee R, Manohar DD, Das DK, Achar A, Mitra A, Chakraborty C (2014) Automated tissue classification framework for reproducible chronic wound assessment. Biomed Res Int 2014:1–9Google Scholar
  2. 2.
    Fauzi MFA, Khansa I, Catignani K, Gordillo G, Sen CK, Gurcan MN (2015) Computerized segmentation and measurement of chronic wound images. Comput Biol Med 60:74–85. [Online]. Available: CrossRefGoogle Scholar
  3. 3.
    Sen CK, Gordillo GM, Roy S, Kirsner R, Lambert L, Hunt TK, Gottrup F, Gurtner GC, Longaker MT (2009) Human skin wounds: a major and snowballing threat to public health and the economy. Wound Repair Regen 17(6):763–771. [Online]. Available: CrossRefGoogle Scholar
  4. 4.
    Cuddigan J, Berlowitz DR, Ayello EA (2001) Pressure ulcers in america: prevalence, incidence, and implications for the future. Adv Skin Wound Care 14(4):208–215Google Scholar
  5. 5.
    Deprez JF, Cloutier G, Schmitt C, Gehin C, Dittmar A, Basset O, Brusseau E (2007) 3d ultrasound elastography for early detection of lesions. evaluation on a pressure ulcer mimicking phantom. In: 2007 29th annual international conference of the ieee engineering in medicine and biology society , pp 79–82Google Scholar
  6. 6.
    Agostini JV, Baker DI, Bogardus ST Making health care safer: A critical analysis of patient safety practices. Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, 2001, no. 27, ch. Prevention of Pressure Ulcers in Older Patients, pp 301–306Google Scholar
  7. 7.
    Leachtenauer J, Kell S, Turner B, Newcomer C, Lyder C, Alwan M (2006) A non-contact imaging-based approach to detecting stage i pressure ulcers. In: the proceedings of the 2006 international conference of the IEEE engineering in medicine and biology society, pp 6380–6383Google Scholar
  8. 8.
    Prado A, Andrades P, Benítez S Cirugía Plástica Esencial. Hospital Clinico Universidad De Chile, 2005, ch. Úlceras por presión, pp 111–126Google Scholar
  9. 9.
    Guadagnin R, Neves RD, Santana LA, Guilhem DB (2014) An image mining based approach to detect pressure ulcer stage. Pattern Recognit Image Anal 24(2):292–296. [Online]. Available: CrossRefGoogle Scholar
  10. 10.
    Beal ME, Smith K (2016) Inpatient pressure ulcer prevalence in an acute care hospital using evidence-based practice. Worldviews Evid-Based Nurs 13(2):112–117. [Online]. Available: CrossRefGoogle Scholar
  11. 11.
    Dorileo AG, Frade MAC, Rangayyan RM, Azevedo-Marques PM (2010) Segmentation and analysis of the tissue composition of dermatological ulcers. In: CCECE 2010, pp 1–4Google Scholar
  12. 12.
    Veredas F, Mesa H, Morente L (2010) Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Trans Med Imaging 29(2):410–427CrossRefGoogle Scholar
  13. 13.
    Azevedo-Marques PM, Pereira SM, Frade MAC, Rangayyan RM (2013) Segmentation of dermatological ulcers using clustering of color components. In: 2013 26th IEEE Canadian conference on electrical and computer engineering (CCECE) , pp 1–4Google Scholar
  14. 14.
    Veredas FJ, Luque-Baena RM, Martín-Santos FJ, Morilla-Herrera JC, Morente L (2015) Wound image evaluation with machine learning. Neurocomputing 164:112–122CrossRefGoogle Scholar
  15. 15.
    Ortiz DP, Sierra-Sosa D, Zapirain BG (2017) Pressure ulcer image segmentation technique through synthetic frequencies generation and contrast variation using toroidal geometry. BioMedical Engineering OnLine 16:1–19CrossRefGoogle Scholar
  16. 16.
    Wang C, Yan X, Smith M, Kochhar K, Rubin M, Warren SM, Wrobel J, Lee H (2015 ) A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks. In: 2015 37th annual international conference of the ieee engineering in medicine and biology society (EMBC), pp 2415–2418Google Scholar
  17. 17.
    Kawahara J, Hamarneh G (2016) Multi-resolution-Tract CNN with hybrid pretrained and skin-lesion trained layers. Springer International Publishing, Cham, pp 164–171Google Scholar
  18. 18.
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. [Online]. Available: CrossRefGoogle Scholar
  19. 19.
    Soliman A, Khalifa F, Elnakib A, El-Ghar MA, Dunlap N, Wang B, Gimel’farb G, Keynton R, El-Baz A (2017) Accurate lungs segmentation on CT chest images by adaptive appearance-guided shape modeling. IEEE Trans Med Imaging 36(1):263– 276CrossRefGoogle Scholar
  20. 20.
    Lindeberg T (2011) Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space. J Math Imaging Vis 40(1):36–81. [Online]. Available: CrossRefGoogle Scholar
  21. 21.
    Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, Rueckert D, Glocker B (2017) Efficient multi-scale 3d {CNN} with fully connected {CRF} for accurate brain lesion segmentation. Med Image Anal 36:61–78CrossRefGoogle Scholar
  22. 22.
    El-Baz A, Elnakib A, Khalifa F, El-Ghar MA, McClure P, Soliman A, Gimelrfarb G (2012) Precise segmentation of 3-d magnetic resonance angiography. IEEE Trans Biomed Eng 59(7):2019–2029CrossRefGoogle Scholar
  23. 23.
    Webb AR, Copsey KD (2011) Statistical pattern recognition, 3rd edn. Wiley, HobokenCrossRefGoogle Scholar
  24. 24.
    Thomas S (2017) Medetec wound database,
  25. 25.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302. [Online]. Available: CrossRefGoogle Scholar
  26. 26.
    Powers DMW (2011) Evaluation: from precision, recall and f-measure to roc, informedness, markedness & correlation. J Mach Learn Technol 2(1):37–63Google Scholar
  27. 27.
    Yang Y, Huang S (2007) Image segmentation by fuzzy c-means clustering algorithm with a novel penalty term. Computing and informatics 26:17–31Google Scholar
  28. 28.
    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9 (1):62–66CrossRefGoogle Scholar
  29. 29.
    Bland J, Altman D (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 8:307–310CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • Begoña García-Zapirain
    • 1
  • Mohammed Elmogy
    • 2
    • 3
  • Ayman El-Baz
    • 3
  • Adel S. Elmaghraby
    • 4
  1. 1.Facultad IngenieríaUniversidad de DeustoBilbaoSpain
  2. 2.Information Technology Department, Faculty of Computers and InformationMansoura UniversityMansouraEgypt
  3. 3.Bioengineering DepartmentUniversity of LouisvilleLouisvilleUSA
  4. 4.Department of Computer Engineering and Computer ScienceUniversity of LouisvilleLouisvilleUSA

Personalised recommendations