Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Wound Infection | Research article

Chronic wound assessment and infection detection method

Authors: Jui-Tse Hsu, Yung-Wei Chen, Te-Wei Ho, Hao-Chih Tai, Jin-Ming Wu, Hsin-Yun Sun, Chi-Sheng Hung, Yi-Chong Zeng, Sy-Yen Kuo, Feipei Lai

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

Login to get access

Abstract

Background

Numerous patients suffer from chronic wounds and wound infections nowadays. Until now, the care for wounds after surgery still remain a tedious and challenging work for the medical personnel and patients. As a result, with the help of the hand-held mobile devices, there is high demand for the development of a series of algorithms and related methods for wound infection early detection and wound self monitoring.

Methods

This research proposed an automated way to perform (1) wound image segmentation and (2) wound infection assessment after surgical operations. The first part describes an edge-based self-adaptive threshold detection image segmentation method to exclude nonwounded areas from the original images. The second part describes a wound infection assessment method based on machine learning approach. In this method, the extraction of feature points from the suture area and an optimal clustering method based on unimodal Rosin threshold algorithm that divides feature points into clusters are introduced. These clusters are then merged into several regions of interest (ROIs), each of which is regarded as a suture site. Notably, a support vector machine (SVM) can automatically interpret infections on these detected suture site.

Results

For (1) wound image segmentation, boundary-based evaluation were applied on 100 images with gold standard set up by three physicians. Overall, it achieves 76.44% true positive rate and 89.04% accuracy value. For (2) wound infection assessment, the results from a retrospective study using confirmed wound pictures from three physicians for the following four symptoms are presented: (1) Swelling, (2) Granulation, (3) Infection, and (4) Tissue Necrosis. Through cross-validation of 134 wound images, for anomaly detection, our classifiers achieved 87.31% accuracy value; for symptom assessment, our classifiers achieved 83.58% accuracy value.

Conclusions

This augmentation mechanism has been demonstrated reliable enough to reduce the need for face-to-face diagnoses. To facilitate the use of this method and analytical framework, an automatic wound interpretation app and an accompanying website were developed.

Trial registration

Appendix
Available only for authorised users
Literature
1.
Sen CK, Gordillo GM, Roy S, Kirsner R, Lambert L, Hunt TK, Gottrup F, Gurtner GC, Longaker MT. Human skin wounds: a major and snowballing threat to public health and the economy. Wound Repair Regen. 2009;17(6):763–71.CrossRef
2.
Dini V, Salvo P, Janowska A, Di Francesco F, Barbini A, Romanelli M. Correlation between wound temperature obtained with an infrared camera and clinical wound bed score in venous leg ulcers. Wounds. 2015;27(10):274–8.PubMed
3.
Lubeley D, Jostschulte K, Kays R, Biskup K, Clasbrummel B. 3D wound measurement system for telemedical applications. In: 39th annual congress of the German society for biomedical engineering, ISSN; 2005. p. 0939–4990.
4.
Hani AFM, Eltegani NM, Hussein SH, Jamil A, Gill P. Assessment of ulcer wounds size using 3D skin surface imaging. In: Visual informatics: bridging research and practice; 2009. p. 243–53.CrossRef
5.
Wannous H, Lucas Y, Treuillet S. Enhanced assessment of the wound-healing process by accurate multiview tissue classification. In: Medical imaging, IEEE transactions on 30.2; 2011. p. 315–26.
6.
Wendelken ME, Berg WT, Lichtenstein P, Markowitz L, Comfort C, Alvarez OM. Wounds measured from digital photographs using photodigital planimetry software: validation and rater reliability. Wounds. 2011;23(9):267–75.PubMed
7.
8.
Oduncu H, Hoppe A, Clark M, Williams RJ, Harding KG. Analysis of skin wound images using digital color image processing: a preliminary communication. Int J Low Extrem Wounds. 2004;3(3):151–6.CrossRef
9.
Plassmann P, Jones TD. Improved active contour models with application to measurement of leg ulcers. J Electron Imaging. 2003;12(2):317–26.CrossRef
10.
Kosmopoulos DI, Tzevelekou FL. Automated pressure ulcer lesion diagnosis for telemedicine systems. IEEE Eng Med Biol Mag. 2007;26(5):18.CrossRef
11.
Hettiarachchi NDJ, Mahindaratne RBH, Mendis GDC, Nanayakkara HT, Nanayakkara ND. Mobile-based wound measurement. In: Proceedings of the IEEE point-of-care healthcare technologies; 2013. p. 298–301.
12.
Zheng H, Bradley L, Patterson D, Galushka M, Winder J. New protocol for leg ulcer tissue classification from colour images. In: Engineering in medicine and biology society, 2004. IEMBS’04. 26th annual international conference of the IEEE, vol. 1; 2004. p. 1389–92.
13.
Mukherjee R, Manohar DD, Das DK, Achar A, Mitra A, Chakraborty C. Automated tissue classification framework for reproducible chronic wound assessment. Biomed Res Int. 2014;2014:1–9.
14.
Song B, Sacan A. Automated wound identification system based on image segmentation and artificial neural networks. In: Proceedings of the IEEE international conference bioinformatics and biomedicine; 2012. p. 1–4.
15.
Wantanajittikul K, Theera-Umpon N, Auephanwiriyakul S, Koanantakool T. Automatic segmentation and degree identification in burn color images. In: Proceedings of the international conference on biomedical engineering; 2011. p. 169–73.
16.
Hani AFM, Arshad L, Malik AS, Jamil A, Yap F. Haemoglobin distribution in ulcers for healing assessment. In: Proceedings of the international conference on intelligent and advanced systems; 2012. p. 362–7.
17.
Veredas F, Mesa H, Morente L. Binary tissue classification on wound images with neural networks and bayesian classifiers. IEEE Trans Med Imaging. 2010;29(2):410–27.CrossRef
18.
Wannous H, Treuillet S, Lucas Y. Supervised tissue classification from color images for a complete wound assessment tool. In: Proceedings of the IEEE EMBS international conference; 2007. p. 6031–4.
19.
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986;8(6):679–98.CrossRef
20.
Oliveira VA, Conci A. Skin detection using HSV color space. In: H. Pedrini, & J. Marques de Carvalho, workshops of Sibgrapi; 2009.
21.
Monteiro FC, Campilho AC. Performance evaluation of image segmentation. Lect Notes Comput Sci. 2006;4141:248–59.CrossRef
22.
Loizou CP, Kasparis T, Mitsi O, Polyviou M. Evaluation of wound healing process based on texture analysis. In: Proc. IEEE Int. Conf. Bioinf. Bioeng.; 2012. p. 709–14.
23.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–6.CrossRef
24.
25.
Dillencourt MB, Samet H, Tamminen M. A general approach to connected-component labeling for arbitrary image representations. J ACM. 1992;39(2):253–80.CrossRef
26.
Dubes RC, Jain AK. Algorithms for clustering data: Prentice Hall; 1988. ISBN:0-13-022278-X.
27.
28.
Coudray N, Buessler, Urban. Robust threshold estimation for images with unimodal histograms. Pattern Recogn Lett. 2010;31(9):1010–9.CrossRef
29.
Voorhees H, Poggio. Detecting textons and texture boundaries in natural images. In: IEEE international conference on computer vision; 1987. p. 250–8.
30.
31.
Hsu C-W, Chang C-C, Lin C-J. A practical guide to support vector classification, technical report. Taipei: Department of Computer Science and Information Engineering, National Taiwan University; 2003. pp. 1-12.
32.
Hsu C-W, Lin C-J. A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw. 2002;13(2):415–25.CrossRef
Metadata
Title
Chronic wound assessment and infection detection method
Authors
Jui-Tse Hsu
Yung-Wei Chen
Te-Wei Ho
Hao-Chih Tai
Jin-Ming Wu
Hsin-Yun Sun
Chi-Sheng Hung
Yi-Chong Zeng
Sy-Yen Kuo
Feipei Lai
Publication date
01-12-2019
Publisher
BioMed Central
Keyword
Wound Infection
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0813-0

Other articles of this Issue 1/2019

BMC Medical Informatics and Decision Making 1/2019 Go to the issue

Correction

Correction to: Automatically identifying social isolation from clinical narratives for patients with prostate Cancer

Research Article

A machine-learning approach to predict postprandial hypoglycemia

Research article

The past, present and future of opioid withdrawal assessment: a scoping review of scales and technologies

Research article

A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia

Study protocol

Promoting healthy teenage behaviour across three European countries through the use of a novel smartphone technology platform, PEGASO fit for future: study protocol of a quasi-experimental, controlled, multi-Centre trial

Research article

Establishing spatially-enabled health registry systems using implicit spatial data pools: case study – Uganda