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Chronic Wound Image Analysis by Particle Swarm Optimization Technique for Tele-Wound Network

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This paper presents an outline of methods that have been proposed for the analysis of chronic wound images. This paper indicates the details of four different ulcerous cases, provides good treatment policy, enhances quality of patient’s life, improves evidence based clinical outcomes and suggests best possible issues. This paper investigates efficient filtering techniques for chronic wound image pre-processing under Tele-wound network. The aim of this work is to accurately access the healing status of chronic wound with improved image processing techniques by proper filtering. Efficient filtering techniques help to reduce the noise for wound images. The simulation results are presented by comparing different parameters. Performance parameters are peak signal to noise ratio (PSNR), mean square error (MSE), signal to noise ratio and mean absolute error. Results shows adaptive Median filtering provides better performances with respect to high PSNR and reduced MSE between original and filtered image. This work proposes the Particle Swarm Optimization (PSO) method for segmentation of wound areas via suitable color space selection. The PSO algorithm in Db channel provided good accuracy (98.93%) for chronic wound segmentation. Here proposed Linier discriminant analysis classifier provides 98% overall tissue prediction accuracy. The aim is to develop telemedicine framework for wound diagnosis by improving good interaction between health experts, patients, and tele-medical agents who belongs to rural/urban areas that are involved in the provision of care to resolve the delayed treatment.

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  1. Vowden, K., Vowden, P., & Posnett, J. (2009). The resource costs of wound care in bradford and airedale primary care trust in the UK. Journal of Wound Care, 18(3), 93–98.

    Article  Google Scholar 

  2. International Diabetes Federation (2006) Diabetes Atlas (3rd ed). Brussels.

  3. Arthur, G. (2010). Wound care: Innovation and chronic care drives market growth. Available at:

  4. Foglia, E., Restelli, U., Napoletano, A. M., Coclite, D., Porazzi, E., Bonfanti, M., et al. (2012). Pressure ulcers management: An economic evaluation. Journal of Preventive Medicine and Hygiene, 53, 30–36.

    Google Scholar 

  5. Harold, B., Jason, M., David, N., Linda, R., David, B., Robert, R., et al. (2010). High cost of stage IV pressure ulcers. The American Journal of Surgery, 200(4), 473–477.

    Article  Google Scholar 

  6. Leaf healthcare, financial overview: The financial impact of pressure ulcers, White Paper, pp. 1–4, 2015.

  7. Gerry, B., Carol, D., & John, P. (2004). The cost of pressure ulcers in the UK. Age and Ageing, 33(3), 230–235.

    Article  Google Scholar 

  8. Gethin, G., & Cowman, S. (2005). Wound measurement comparing the use of acetate tracings and Visitrak digital planimetry. Journal of Clinical Nursing, 15, 422–427.

    Article  Google Scholar 

  9. Heather, O., David, K., Louise, F., & Magie, F. (2004). The basic principles of wound healing. Wound Care Canada, 9(2), 1–8.

    Google Scholar 

  10. Norton, D. (1989). Calculating the risk: Reflection on the Norton scale. Decubitus, 2(3), 24–31.

    Google Scholar 

  11. Gosnell, D. (1989). Pressure sore risk management: A critique part 1 the Gosnell scale. Decubitus, 2(3), 32–38.

    Google Scholar 

  12. Bergstrom, N., Braden, B., Laguzza, A., & Hollman, V. (1985). The braden scale for predicting pressure sore risk. Nursing Research, 36, 205–210.

    Google Scholar 

  13. Bates, J. B., Vredevoe, V., & Becht, M. (1992). Validity and reliability of the pressure sore status tool. Decubitus, 5, 20–28.

    Google Scholar 

  14. Albouy, B., Treuillet, S., Lucas, Y., Barre, H., & Pichaud, J. C. (2005). Depth and color analysis of wounds using digital camera. ITBM-RBM, 26(4), 240–242.

    Article  Google Scholar 

  15. Chakraborty, C., Gupta, B., & Ghosh, S. K. (2014). Mobile metadata assisted community database of chronic wound. Wound Medicine, 6, 34–42.

    Article  Google Scholar 

  16. Berriess, W. P., & Sangwine, S. J. (1997). A colour histogram clustering technique for tissue analysis of healing skin wounds. IPA97, 443, 693–697.

    Google Scholar 

  17. Hoppe, A., Wertheim, D., Melhuish, J., Harding, K. G., Williams, R. J. (2000). A spline based method for assessment of wound images. In 1st International Conference on Advances in Medical Signal and Information Processing, pp. 206–211.

  18. Jones, T. D., & Plassmann, P. (2000). An active contour model for measuring the area of leg ulcers. IEEE Transactions on Medical Imaging, 19(12), 1202–1210.

    Article  Google Scholar 

  19. Duckworth, M., Patel, N., Joshi, A., Lankton, S. (2007). Clinically affordable non-contact wound measurement device. In 30th RESNA Conference on Technology and Disability, pp. 1–3.

  20. Perez, A. A., & Gonzaga, A. (2001). Assessment of leg ulcer color images through digital image processing. Technology and Health Care, 9(1), 28–30.

    Google Scholar 

  21. Aslantas, V., Tunckanat, M. (2007). Differential evolution algorithm for segmentation of wound images. In IEEE International Symposium on Intelligent Signal Processing, pp. 1–5.

  22. Chakraborty, C., Gupta, B., Ghosh, S. K., Das, D., & Chakraborty, C. (2016). Telemedicine supported chronic wound tissue prediction using different classification approach. Journal of Medical Systems, 40(3), 1–12.

    Article  Google Scholar 

  23. Janice, Z. C. (1988). The new RYB color code. American Journal of Nursing, 88(10), 1342–1346.

    Google Scholar 

  24. Sheehan, P., Jones, P., Caselli, A., Giurini, J. M., & Veves, A. (2003). Percent change in wound area of diabetic foot ulcers over a 4-week period is a robust predictor of complete healing in a 12-week prospective trial. Diabetes Care, 26, 1879–1882.

    Article  Google Scholar 

  25. Sanada, H., Moriguchi, T., Miyachi, Y., Ohura, T., Nakajo, T., Tokunaga, K., et al. (2004). Reliability and validity of DESIGN, a tool that classifies pressure ulcer severity and monitors healing. Journal of Wound Care, 13(1), 13–18.

    Article  Google Scholar 

  26. Bates, J. M. (1994). The pressure sore status tool: An outcome measure for pressure sores. Top Geriatric Rehabil., 9(4), 17–34.

    Article  Google Scholar 

  27. Bates, J., Vredevoe, D. L., & Brecht, M. L. (1992). Validity and reliability of the pressure sore status tool. Decubitus, 5(6), 20–28.

    Google Scholar 

  28. Thomas, D. R., Rodeheaver, G. T., Bartolucci, A. A., Franz, R. A., Sussman, C., Ferrell, B. A., et al. (1997). Pressure ulcer scale for healing: Derivation and validation of the PUSH tool. The PUSH Task Force. Advances in Skin & Wound Care, 10(5), 96–101.

    Google Scholar 

  29. Ferrell, B. A., Artinian, B. M., & Sessing, D. (1995). The Sessing Scale for assessment of pressure ulcer healing. Journal of the American Geriatric Society, 43, 37–40.

    Article  Google Scholar 

  30. Krasner, D. (1997). Wound healing scale, version 1.0: A proposal. Advanced Wound Care, 10(5), 82–85.

    Google Scholar 

  31. Sanada, H., Moriguchi, T., Miyachi, Y., Ohura, T., Nakajo, T., Tokunaga, K., et al. (2004). Reliability and validity of DESIGN, a tool that classifies pressure ulcer severity and monitors healing. Journal of Wound Care, 13(1), 13–18.

    Article  Google Scholar 

  32. Emparanza, J. L., Aranegui, P., & Ruiz, M. (2000). A simple severity index for pressure ulcers. Journal of Wound Care, 9(2), 86–90.

    Article  Google Scholar 

  33. Houghton, P. E., Kincaid, C. B., Campbell, K. E., Woodbury, M. G., & Keast, D. H. (2000). Photographic assessment of the appearance of chronic pressure and leg ulcers. Ostomy/Wound Manage, 46(4), 20–30.

    Google Scholar 

  34. Sussman, C., & Swanson, G. (1997). Utility of the sussman wound healing tool in predicting wound healing outcomes in physical therapy. Advances in Skin & Wound Care, 10(5), 74–77.

    Google Scholar 

  35. Medical therapy. Available at: Accessed on March 1, 2016.

  36. Nelson, E. A., Bell, S. E., Cullum, N. A. (2012). Compression for preventing recurrence of venous ulcers. Cochrane Database System Review , 11, CD000265.

  37. Abbade, L. P., & Lastoria, S. (2005). Venous ulcer: Epidemiology, physiopathology, diagnosis and treatment. International Journal of Dermatology, 44, 449–456.

    Article  Google Scholar 

  38. Fife, C., Walker, D., Thomson, B., & Carter, M. (2007). Limitations of daily living activities in patients with venous stasis ulcers undergoing compression bandaging: Problems with the concept of self-bandaging. Journal of Wounds, 19, 55–57.

    Google Scholar 

  39. Johnson, M. (1995). Healing determinants in older people with leg ulcers. Research in Nursing & Health, 18, 395–403.

    Article  Google Scholar 

  40. Romanelli, M. (1997). Objective measurement of venous ulcer debridement and granulation with skin colour reflectance analyzer. Wounds, 9(4), 122–126.

    Google Scholar 

  41. Chakraborty, C., Gupta, B., Ghosh, S. K. (2016). Mobile telemedicine systems for remote patient’s chronic wound monitoring, M-Health Innovations for Patient-Centered Care, Ch.11, pp. 217–243.

  42. Medetec wound database: Available via.

  43. Luca, D. P., Enrico, B., Antonella, S., Maurizio, M., Daniele, D. V., Roberta, A., et al. (2006). Super-oxidized solution (SOS) therapy for infected diabetic foot ulcers. Journal of Wounds, 18(9), 262–270.

    Google Scholar 

  44. Kulbir, D. (2014). Managing venous ulcers: Compression therapy, local wound care, dressing, antibiotics, surgery and adjunctive methods play a role in management. Wound Care Advisor , 3(1), 12–19.

  45. Fonder, M. A., Lazarus, G. S., Cowan, D. A., Aronson, C. B., Kohli, A. R., & Mamelak, A. J. (2008). Treating the chronic wound: A practical approach to the care of nonhealing wounds and wound care dressings. Journal of American Academy of Dermatology, 58(2), 185–206.

    Article  Google Scholar 

  46. Szycher, M., & Lee, S. J. (1992). Modern wound dressings: A systematic approach to wound healing. Journal of Biomaterials Applications, 7, 142–213.

    Article  Google Scholar 

  47. Fletcher, J. (2005). Understanding wound dressings: Alginates. Nursing Times, 101, 53–54.

    Google Scholar 

  48. Chakraborty, C., Gupta, B., & Ghosh, S. (2014). Mobile metadata assisted community database of chronic wound. International Journal of Wound Medicine, 6, 34–42.

    Article  Google Scholar 

  49. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial systems. Oxford: Oxford University Press.

    MATH  Google Scholar 

  50. Kennedy, J., Eberhart, R. (2001). Particle swarm optimization, developments, applications and resources. IEEE.

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Correspondence to Chinmay Chakraborty.

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Chakraborty, C. Chronic Wound Image Analysis by Particle Swarm Optimization Technique for Tele-Wound Network. Wireless Pers Commun 96, 3655–3671 (2017).

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