Computer Aided Wound Area Detection System for Dermatological Images

  • Sümeyya İlkin
  • Fidan Kaya Gülağız
  • Fatma Selin Hangişi
  • Suhap Şahin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)

Abstract

Research shows that in the last decade, the focus on computer-assisted diagnoses on the skin disorders has increased significantly as a result of the improvements in skin imaging technology and the development of compatible image processing techniques. More accurate treatments provided by means of computer-assisted diagnostic systems increase the patients’ chances of recovery and survival. Image processing techniques used in these systems facilitate the detection of wound areas. In this study, a wound detection system using adaptive weighted median filter (AWMF), Otsu’s thresholding, and an implementation of the Canny edge detection algorithm using the Sobel kernel, respectively, is proposed for the detection of wound areas on dermatological images. The effectiveness of the system is tested on different dermatological datasets. Obtained values are analyzed with Peak Signal to Nose Ratio (PSNR) and Correlation Coefficient (CC) metrics and it was confirmed that the system works accurately on various datasets.

Keywords

Medical image processing Wound area detection Dermatologic images Thresholding Filtering Edge detection Computer based diagnostic systems 

References

  1. 1.
    Gutman, D., Codella, N.C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., Halpern, A.: Skin lesion analysis toward melanoma detection. In: Society for Melanoma Research Congress. Boston, Massachusetts (2016)Google Scholar
  2. 2.
    Flores, E., Scharcanski, J.: Segmentation of melanocytic skin lesions using feature learning and dictionaries. Expert Syst. Appl. 56, 300–309 (2016)CrossRefGoogle Scholar
  3. 3.
    Maier, T., Kulichova, D., Schotten, K., Astrid, R., Ruzicka, T., Berking, C., Udrea, A.: Accuracy of a smartphone application using fractal image analysis of pigmented moles compared to clinical diagnosis and histological result. J. Eur. Acad. Dermatol. Venereol. 29, 663–667 (2015)CrossRefGoogle Scholar
  4. 4.
    Tan, T.Y., Zhang, L., Jiang, M.: An intelligent decision support system for skin cancer detection from dermoscopic images. In: 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 2194–2199. IEEE Press, Changsha, China (2016)Google Scholar
  5. 5.
    Abbas, A.A., Guo, X., Tan, W.H., Jalab, H.A.: Combined spline and b-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel. J. Med. Syst. 38(8), 1–8 (2014)CrossRefGoogle Scholar
  6. 6.
    Mishra, N.K., Celebi, M.E.: An overview of melanoma detection in dermoscopy images using image processing and machine learning. CoRR. 1601.07843 (2016)Google Scholar
  7. 7.
  8. 8.
    Ganster, H., Pinz, A., Röhrer, R., Wildling, E., Binder, M., Kittler, H.: Automated melanoma recognition. IEEE Trans. Med. Imaging 20(3), 233–239 (2001)CrossRefGoogle Scholar
  9. 9.
    Filko, D., Nyarko, E.K., Cupec, R.: Wound detection and reconstruction using RGB-D Camera. In: 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1217–1222. IEEE Press, Opatija, Croatia (2016)Google Scholar
  10. 10.
    Gethin, G., Cowman, S.: Wound measurement comparing the use of acetate tracings and visitrak digital planimetry. J. Clin. Nurs. 15(4), 422–427 (2005)CrossRefGoogle Scholar
  11. 11.
    Gilman, T.: Wounds outcomes: the utility of surface measures. Lower Extremity Wounds 3(3), 125–132 (2004)CrossRefGoogle Scholar
  12. 12.
    Filko, D., Antonic, D., Huljev, D.: WITA - application for wound analysis and management. In: 12th IEEE International Conference on e-Health Networking Applications and Services, pp. 68–73. Lyon, France (2010)Google Scholar
  13. 13.
    Mukherjee, R., Manohar, D.D., Das, D.K., Achar, A., Mitra, A., Chakraborty, C.: Automated tissue classification framework for reproducible chronic wound assessment. Biomed. Res. Int. 2014, 1–9 (2014)Google Scholar
  14. 14.
    Wang, C., Yan, X., Smith, M., Kochhar, K., Rubin, M., Warren, S.M., Wrobel, J., Lee, H.: A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2415–2418. IEEE Press, Milan, Italy (2015)Google Scholar
  15. 15.
    Jannin, P., Krupinski, E., Warfield, S.K.: Validation in medical image processing. IEEE Trans. Med. Imaging 25(11), 1405–1409 (2006)CrossRefGoogle Scholar
  16. 16.
    Maglogiannis, I., Pavlopoulos, S., Koutsouris, D.: An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Trans. Inf Technol. Biomed. 9(1), 86–98 (2005)CrossRefGoogle Scholar
  17. 17.
    Celebi, M.E., Zornberg, A.: Automated quantification of clinically significant colors in dermoscopy images and its application to skin lesion classification. IEEE Syst. J. 8(3), 980–984 (2014)CrossRefGoogle Scholar
  18. 18.
    Handels, H., Mersmann, S., Palm, C., Tolxdorff, T., Wagenknecht, G., Wittenberg, T.: Viewpoints on medical image processing: from science to application. Current Medical Imaging Reviews. 9(2), 79–88 (2013)CrossRefGoogle Scholar
  19. 19.
    Jafari, M.H., Samavi, S., Karimi, N., Soroushmehr, S.M., Ward, K., Najarian, K.: Automatic detection of melanoma using broad extraction of features from digital images. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1357–1360. IEEE Press, Orlando, USA (2016)Google Scholar
  20. 20.
    Walter, T., Klein, J.C., Massin, P., Erginay, A.: A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging 21(10), 1236–1243 (2002)CrossRefGoogle Scholar
  21. 21.
    Fonseca-Pinto, R., Machado, M.: A textured scale-based approach to melanocytic skin lesions in dermoscopy. In: 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia (2017)Google Scholar
  22. 22.
    Ozkan, H., Gurleyen, R., Usta, E., Kumrular, R.K.: Automatic skin lesion segmentation. In: Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT), Istanbul, Turkey (2017)Google Scholar
  23. 23.
    Nasr-Esfahani, E. et al.: Melanoma detection by analysis of clinical images using convolutional neural network. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL (2016)Google Scholar
  24. 24.
    Attia, M., Hossny, M., Nahavandi, S., Yazdabadi, A.: Skin melanoma segmentation using recurrent and convolutional neural networks. In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, VIC (2017)Google Scholar
  25. 25.
    Cueva, W.F., Muñoz, F., Vásquez, G., Delgado, G.: Detection of skin cancer “Melanoma” through computer vision. In: IEEE 24th International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru (2017)Google Scholar
  26. 26.
    Suganya, R.: An automated computer aided diagnosis of skin lesions detection and classification for dermoscopy images. In: International Conference on Recent Trends in Information Technology (ICRTIT), Chennai, India (2016)Google Scholar
  27. 27.
    Santy A., Joseph, R.: Segmentation methods for computer aided melanoma detection. In: Global Conference on Communication Technologies (GCCT), Thuckalay, India (2015)Google Scholar
  28. 28.
    Altuncu, M.A., Kaya Gülağız, F., Hangişi, F.S., Şahin, S.: Performance analysis of image restoration techniques for dermoscopy images. IJAIS 11(8), 15–18 (2017)CrossRefGoogle Scholar
  29. 29.
    İlkin, S., Hangişi, F.S., Şahin, S.: Comparison of global histogram-based thresholding methods that applied on wound images. Int. J. Comput. Appl. 165(9), 23–28 (2017)Google Scholar
  30. 30.
    İlkin, S., Hangişi, F.S., Tafralı, M., Şahin, S.: The enhancement of canny edge detection algorithm using prewitt, robert and sobel kernels. In: International Conference on Engineering Technologies. Konya, Turkey (2017, Accepted)Google Scholar
  31. 31.

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sümeyya İlkin
    • 1
  • Fidan Kaya Gülağız
    • 1
  • Fatma Selin Hangişi
    • 1
  • Suhap Şahin
    • 1
  1. 1.Department of Computer EngineeringKocaeli UniversityUmuttepeTurkey

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