skip to main content
10.1145/2093698.2093766acmotherconferencesArticle/Chapter ViewAbstractPublication PagesisabelConference Proceedingsconference-collections
research-article

Classification of dermatological ulcers based on tissue composition and color texture features

Published: 26 October 2011 Publication History

Abstract

We present color image processing methods for the analysis of images of dermatological lesions. The intended application is classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yellow), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist into the classes mentioned above. Indexing of the images was performed based on statistical texture features derived from cooccurrence matrices of the RGB, HSV, L*a*b*, and L*u*v* color components. The classification was performed using different classifiers and database organization methods. The performance of classification was measured in terms of the area under the receiver operating characteristic curve, with values of up to 0.98 for the granulation and fibrin classes.

References

[1]
V. Arvis, C. Debain, M. Berducat, and A. Benassi. Generalization of the cooccurrence matrix for colour images: application to colour texture classification. Image Analysis and Stereology, 23(1):63--72, 2004.
[2]
L. Ballerini, X. Li, R. Fisher, and J. Rees. A query-by-example content-based image retrieval system of non-melanoma skin lesions. Medical Content-Based Retrieval for Clinical Decision Support, pages 31--38, 2010.
[3]
K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? In C. Beeri and P. Buneman, editors, Database Theory - ICDT'99, volume 1540 of Lecture Notes in Computer Science, pages 217--235. Springer Berlin/Heidelberg, 1999.
[4]
K. Burnand, I. Whimster, and A. Naidoo. Pericapillary fibrin in the ulcer-bearing skin of the leg: the cause of lipodermatosclerosis and venous ulceration. British Medical Journal (Clinical Research ed.), 285(6348):1071--1072, 1982.
[5]
R. W. Conners and C. A. Harlow. A theoretical comparison of texture algorithms. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-2(3):204--222, 1980.
[6]
T. Dietterich. Overfitting and undercomputing in machine learning. ACM Comput. Surv., 27:326--327, September 1995.
[7]
P. Domingos and M. J. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29(2):103--130, 1997.
[8]
E. Dorileo, M. Frade, R. Rangayyan, and P. Azevedo-Marques. Segmentation and analysis of the tissue composition of dermatological ulcers. In Electrical and Computer Engineering (CCECE), 2010 23rd IEEE Canadian Conference on, pages 1--4. IEEE.
[9]
E. Dorileo, M. Frade, A. Roselino, R. Rangayyan, and P. Azevedo-Marques. Color image processing and content-based image retrieval techniques for the analysis of dermatological lesions. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pages 1230--1233. IEEE, 2008.
[10]
R. Gonzalez and R. Woods. Digital Image Processing. Pearson Prentice Hall, 3 edition, 2008.
[11]
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The Weka data mining software: an update. ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) Explorations Newsletter, 11(1):10--18, 2009.
[12]
R. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. Systems, Man and Cybernetics, IEEE Transactions on, 3(6):610--621, 1973.
[13]
S. Herrick, P. Sloan, M. McGurk, L. Freak, C. McCollum, and M. Ferguson. Sequential changes in histologic pattern and extracellular matrix deposition during the healing of chronic venous ulcers. The American Journal of Pathology, 141(5):1085--1095, 1992.
[14]
R. Kohavi. A study of cross-validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on Artificial Intelligence, volume 14, pages 1137--1145. Citeseer, 1995.
[15]
I. Maglogiannis, S. Pavlopoulos, and D. Koutsouris. An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. Information Technology in Biomedicine, IEEE Transactions on, 9(1):86--98, 2005.
[16]
H. Murray, M. Young, S. Hollis, and A. Boulton. The association between callus formation, high pressures and neuropathy in diabetic foot ulceration. Diabetic Medicine, 13(11):979--982, 1996.
[17]
H. Oduncu, A. Hoppe, M. Clark, R. Williams, and K. Harding. Analysis of skin wound images using digital color image processing: a preliminary communication. The International Journal of Lower Extremity Wounds, 3(3):151--156, 2004.
[18]
T. Pavicic and H. Korting. Xerosis and callus formation as a key to the diabetic foot syndrome: dermatologic view of the problem and its management. JDDG: Journal der Deutschen Dermatologischen Gesellschaft, 4(11):935--941, 2006.
[19]
J. Quinlan. Decision trees and decision-making. Systems, Man and Cybernetics, IEEE Transactions on, 20(2):339--346, 1990.
[20]
M. Rezvani, M. Robbins, J. Hopewell, and E. Whitehouse. Modification of late dermal necrosis in the pig by treatment with multi-wavelength light. British Journal of Radiology, 66(782):145--149, 1993.
[21]
F. Sebastiani. Machine learning in automated text categorization. ACM Comput. Surv., 34:1--47, March 2002.
[22]
A. Tarallo, A. Gonzaga, and M. Frade. Artificial neural networks applied to the segmentation and classification of digital images of cutaneous ulcers. In IEEE 7th International Conference on Bioinformatics and Bioengineering, pages 1--1, 2007.
[23]
N. Vaziri, A. Hojabri, A. Erfani, M. Monsefi, and B. Nilforooshan. Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study. Nuclear Engineering and Design, 237(4):377--385, 2007.
[24]
H. Wannous, S. Treuillet, and Y. Lucas. Robust tissue classification for reproducible wound assessment in telemedicine environments. Journal of Electronic Imaging, 19:023002, 2010.
[25]
M. Witte and A. Barbul. General principles of wound healing. Surgical Clinics of North America, 77(3):509--528, 1997.
[26]
Y. Yuan and M. J. Shaw. Induction of fuzzy decision trees. Fuzzy Sets and Systems, 69(2):125--139, 1995.

Cited By

View all
  • (2018)A computational method for semi‐automatic measurement of pressure ulcersWound Repair and Regeneration10.1111/wrr.1265026:4(332-339)Online publication date: 22-Oct-2018
  • (2018)Selection of Colour Correction Algorithms for Calibrating Optical Chronic Ulcer ImagesAdvanced Computational and Communication Paradigms10.1007/978-981-10-8240-5_63(561-570)Online publication date: 8-Jun-2018
  • (2015)Computer assessment of the composition of a generic wound by image processingPlastic and Aesthetic Research10.4103/2347-9264.1654442:5(261)Online publication date: 2015
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
October 2011
949 pages
ISBN:9781450309134
DOI:10.1145/2093698
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

  • Universitat Pompeu Fabra
  • IEEE
  • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
  • River Publishers: River Publishers
  • CTTC: Technological Center for Telecommunications of Catalonia
  • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2011

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. color image processing
  2. color texture
  3. dermatological ulcers
  4. machine learning
  5. pattern recognition
  6. tissue composition

Qualifiers

  • Research-article

Funding Sources

Conference

ISABEL '11
Sponsor:
  • Technical University of Catalonia Spain
  • River Publishers
  • CTTC
  • CTIF

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2018)A computational method for semi‐automatic measurement of pressure ulcersWound Repair and Regeneration10.1111/wrr.1265026:4(332-339)Online publication date: 22-Oct-2018
  • (2018)Selection of Colour Correction Algorithms for Calibrating Optical Chronic Ulcer ImagesAdvanced Computational and Communication Paradigms10.1007/978-981-10-8240-5_63(561-570)Online publication date: 8-Jun-2018
  • (2015)Computer assessment of the composition of a generic wound by image processingPlastic and Aesthetic Research10.4103/2347-9264.1654442:5(261)Online publication date: 2015
  • (2013)Adaptive schemes for expanded diagnosis image evaluation2013 Pan American Health Care Exchanges (PAHCE)10.1109/PAHCE.2013.6568352(1-4)Online publication date: Apr-2013

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media