Page 274 - Read Online

Table 2: %Match of the visual and computer assessments, 2. Herbin M, Bon FX, Venot A, Jeanlouis F, Dubertret ML, Dubertret L,
for various categories of tissue and pigmentation Strauch G. Assessment of healing kinetics through true color image
processing. IEEE Trans Med Imaging 1993;12:39‑43.
Tissue/pigmentation category MD RCS 3. Berris WP, Sangwine SJ. Automatic Quantitative Analysis of Healing Skin
HGT 100 100 Wounds Using Colour Digital Image Processing. World Wide Wounds,
UGT 76.6 89.99 1997; Available from: http://www.worldwidewounds.com/1997/july/Berris/
Berris.html. [Last accessed on 2015 Apr 12].
WS 80.5 94.60 4. Mekkes JR, Westerhof W. Image processing in the study of wound healing.
G 1 98.4 95.75 Clin Dermatol 1995;13:401‑7.
G 2 89.3 92.26 5. Jones BF, Plassmann P. An instrument to measure the dimensions of skin
F 96.1 98.11 wounds. IEEE Trans Biomed Eng 1995;42:464‑70.
BNT 98.2 100 6. Hansen GL, Sparrow EM, Kokate JY, Leland KJ, Iaizzo PA. Wound
Ga 93.3 78.94 status evaluation using color image processing. IEEE Trans Med Imaging
1997;16:78‑86.
MD: The method based on Mahalanobis distance, RCS: The rotated coordinate 7. Berris WP, Sangwine SJ. A Colour Histogram Clustering Technique for
system method, HGT: Healthy granulation tissue, UGT: Unhealthy granulation Tissue Analysis of Healing Skin Wounds. Proceedings of the 6th International
tissue, WS: Whitish slough, G : Yellowish green pigmentation, G : Bluish green Conference on Image Processing and its Applications, 1997 July 14‑17. Vol. 2.
2
1
pigmentation, F: Fat, BNT: Brown necrotic tissue, Ga: Gangrene Dublin, New York: IET; 1997. p. 693‑7.
8. Hoppe A, Wertheim D, Melhuish J, Morris H, Harding KG, Williams RJ.
would be useful to take a consensus of multiple operators Computer Assisted Assessment of Wound Appearance Using Digital Imaging.
for segmenting clusters for training, and use different In: Proceedings of the 23rd Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, 2001 October, 25‑28. Istanbul,
operators for selecting ROIs for a more robust validation Turkey; 2001. p. 2595‑7.
of the algorithms. 9. Oduncu H, Hoppe A, Clark M, Williams RJ, Harding KG. Analysis of
skin wound images using digital color image processing: a preliminary
In conclusion, this paper establishes eight categories of communication. Int J Low Extrem Wounds 2004;3:151‑6.
color due to tissue types and pigmentation, more than 10. Varedas FJ, Mesa H, Morente L. A hybrid learning approach to tissue
those based on the commonly used 4‑color model. The recognition in wound images. Int J Intell Comput Cybern 2009;2:327‑47.
results were based on a knowledge base built using 11. Wannous H, Trelluillet S, Lucas Y. Robust tissue classification for reproducible
wound assessment in telemedicine environments. J Electron Imaging
the one‑to‑one correspondence between tissue types 2010;19:1‑9.
pigmentation, and color. The (modified) HSI model 12. Dorileo EAG, Frade MAC, Rangayyan RM, Azevedo Marques PM.
was used because it better represents the physician’s "Segmentation and Analysis of Tissue Composition of Dermatological
perception of color, in addition, to resolving the Ulcers", Proceedings of the Canadian Conference on Electrical and Computer
Engineering, Calgary, Canada, May 2010. p. 1‑4.
information into eight useful categories. The resulting 13. Pereira SM, Frade MA, Rangayyan RM, Marques PM. Classification of
eight categories provide a better representation and Dermatological Ulcers Based on Tissue Composition and Color Texture
assessment of wound health and minimize error in Features. Proceedings of the 4th International Symposium on Applied
judgment due to misclassification of unidentified tissue Sciences in Biomedical and Communication Technologies, 2011 October
26‑29. Barcelona, Spain. New York: ACM; 2011. http://dl.acm.org/citation.
types and pigmentation. Segmentation of wounds would cfm?id=2093766&preflayout=tabs. [Last accessed on 2015 Aug 27].
be very useful for monitoring and objective recording of 14. Nayak R, Kumar P, Galigekere RR. Towards a Comprehensive Assessment
various phases of wound healing and the response to of Wound Composition by Color Image Processing. Proceedings of the
treatment protocols. 4th International Conference on Image Processing, 2009 November 7‑10.
Cairo, Egypt, New York: IEEE; 2009. p. 4185‑8.
Acknowledgments 15. Veredas F, Mesa H, Morente L. Binary classification on wound images
We are grateful to the critical remarks and suggestions with neural networks and bayesian classifiers. IEEE Trans Med Imaging
2010;29:410‑27.
of the reviewers, who have gone through the material 16. Mukherjee R, Manohar DD, Das DK, Achar A, Mitra A, Chakraborty C.
in great detail. The data reported in this paper were Automated tissue classification framework for reproducible chronic wound
collected by the second author while he was the Head assessment. Biomed Res Int 2014;2014:851582.
of the Department of Plastic Surgery and Burns, Kasturba 17. Kolesnik M, Fexa A. How Robust is SVM Wound Segmentation? Proceedings
of the 7th Nordic Symposium on Signal Processing, 2006 June, 7‑9. Rejkjavik,
Medical College, Manipal University, Manipal. New York: IEEE; 2006. p. 50‑3.
18. Gonzalez RC, Woods RE. Digital Image Processing. 3rd ed. Delhi: Prentice
Financial support and sponsorship Hall; 2007.
Nil. 19. Dubois SR, Glanz FH. An autoregressive model approach to two‑dimensional
shape classification. IEEE Trans Pattern Anal Mach Intell 1986;8:55‑66.
Conflicts of interest 20. Herbin M, Venot A, Devaux JY, Piette C. Color quantitation through image
There are no conflicts of interest. processing in dermatology. IEEE Trans Med Imaging 1990;9:262‑9.
21. Raykov T, Marcoulides GA. An Introduction to Applied Multivariate Analysis.
New York: Routledge Taylor and Francis Group; 2008.
REFERENCES 22. Duda RO, Hart PE, Stork DG. Pattern Classification. 2nd ed. Hoboken:
Wiley; 2001.
1. Arnqvist J, Hellgren L, Vincent J. Semiautomatic Classification of Secondary 23. Gashaw A, Mohammed H, Singh H. Genetic divergence in selected durum
Healing Ulcers in Multispectral Images. Proceedings of 9th International wheat genotypes of Ethiopian plasm. Afr Crop Sci J 2007;15:67‑72.
Conference on Pattern Recognition, 1988 November, 14‑17. Rome, 24. Hertzog C. On pooling covariance matrices for multivariate analysis.
New York: IEEE; 1988. p. 459‑61. Educ Psychol Meas 1986;46:349‑52.

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