1. 1. K. M. Buckley, L. K. Adelson, and J. G. Agazio, “Reducing the risks of wound consultation: Adding digital images to verbal reports,” Wound Ostomy Continence Nurs., vol. 36, no. 2, pp. 163–170, Mar. 2009.
   2. V. Falanga, “The chronic wound: Impaired healing and solutions in the context of wound bed preparation,” Blood Cells Mol. Dis., vol. 32, no. 1, pp. 88–94, Jan. 2004
   3. C. T. Hess and R. S. Kirsner, “Orchestrating wound healing: Assessing and preparing the wound bed,” J. Adv. Skin Wound Care, vol. 16, no. 5, pp. 246–257, Sep. 2006
   4. R. S. Rees and N. Bashshur, “The effects of tele wound management on use of service and financial outcomes,” Telemed. J. E. Health, vol. 13, no. 6, pp. 663–674, Dec. 2007.
   5. NIH’s National Diabetes Information Clearing House, National Institute of Health. (2011). [Online]. Available: www.diabetes.niddk.nih.gov
   6. H. Wannous, Y. Lucas, and S. Treuillet, “Combined machine learning with multi-view modeling for robust wound tissue assessment,” in Proc. 5th Int. Conf. Comp. Vis. Theory Appl., May 2010, pp. 98–104.
   7. H.Wannous,Y. Lucas, S. Treuillet, andB.Albouy, “A complete 3Dwound assessment tool for accurate tissue classification and measurement,” in Proc. IEEE 15th Conf. Image Process., Oct. 2008, pp. 2928–2931.
   8. P. Plassman and T. D. Jones, “MAVIS: A noninvasive instrument to measure area and volume of wounds,” Med. Eng. Phys., vol. 20, no. 5, pp. 332– 338, Jul. 1998.
   9. A. Malian, A. Azizi, F. A. Van den Heuvel, and M. Zolfaghari, “Development of a robust photogrammetric metrology system for monitoring the healing of bedscores,” Photogrammetric Rec., vol. 20, no. 111, pp. 241–273, Jan. 2005.
   10. H. Wannous, S. Treuillet, and Y. Lucas, “Robust tissue classification for reproduciblewound assessment in telemedicine environment,” J. Electron. Imag., vol. 19, no. 2, pp. 023002-1 023002- 9, Apr. 2010.
   11. H. Wannous, Y. Lucas, and S. Treuillet, “Supervised tissue classification from color images for a complete wound assessment tool,” in Proc. IEEE 29th Annu. Int. Conf. Eng. Med. Biol. Soc., Aug. 2007, pp. 6031–6034.
   12. N. Kabelev. Machine vision enables detection of melanoma at most curable stage, MELA Sciences, Inc., Irvington, NY, USA. (2013, May). [Online]. Available: http://www.medicaldesignbriefs.com/ component/content/article/1105-mdb/features/16364
   13. L. T. Kohn, J. M. Corrigan, and M. S. Donaldson, Crossing the Quality Chasm: A New Health System for the 21st Century Health Care Services. Washington, DC, USA: Nat. Acad. Press, 2001.
   14. L. Wang, P. C. Pedersen, D. Strong, B. Tulu, and E. Agu, “Wound image analysis system for diabetics,” Proc. SPIE, vol. 8669, pp. 866924-1– 866924-14, Feb. 2013.
   15. C. M. Li, C. Y. Xu, and C. F. Gui, “Distance regularized level set evolution and its application to image segmentation,” IEEE Trans. Image Process., vol. 19, no. 12, pp. 3243 3254, Dec. 2010.