Author : K Lakshmi Priya 1
Date of Publication :7th March 2016
Abstract: Diabetic foot ulcers represent a serious health issue. Today clinicians and nurses produce wound assessment by observing the wound size and healing status visually. Here the patients also have an opportunity to play an active role. This method enables the patients and clinicians to take a more active role in daily wound care which can quicken wound healing and also saves the travel cost, reduce healthcare expenses. As the pervasiveness of Smartphones with a highresolution digital camera, assessing wounds by analyzing the images of constant foot ulcers. A wound image analysis system is implemented on the android smart phone. The wound image is occupied by the camera on the smart phone with the help of an image capture box. Later that, the smart phone performs wound segmentation by applying the accelerated mean-shift algorithm. The outline of the foot is identified based on skin color, and the wound boundary is recognized using a simple connected region detection method. The healing status is beside assessed based on redyellowblack color evaluation model with in the boundary of the wound. Further, the healing status is significantly assessed, based on the analysis of patients time records. The test results on wound images collected in UMASSMemorial Health Center Wound Clinic (Worcester, MA).
- 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.
- 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
- 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
- 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.
- NIH’s National Diabetes Information Clearing House, National Institute of Health. (2011). [Online]. Available: www.diabetes.niddk.nih.gov
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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
- 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.
- 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.
- 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.