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An Improved Segmentation Method for Non-melanoma Skin Lesions Using Active Contour Model

  • Qaisar Abbas
  • Irene FondónEmail author
  • Auxiliadora Sarmiento
  • M. Emre Celebi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8815)

Abstract

Computer-Aided Diagnosis (CAD) systems are widely used to classify skin lesions in dermoscopic images. The segmentation of the lesion area is the initial and key step to automate this process using a CAD system. In this paper, an improved segmentation algorithm is developed based on the following steps: (1) color space transform to the perception-oriented CIECAM02 color model, (2) preprocessing step to correct specular reflection, (3) contrast enhancement using an homomorphic transform filter (HTF) and nonlinear sigmoidal function (NSF) and (4) segmentation with relative entropy (RE) and active contours model (ACM). To validate the proposed technique, comparisons with other three state-of-the-art segmentation algorithms were performed for 210 non-melanoma lesions. From these experiments, an average true detection rate of 91.01, false positive rate of 6.35 and an error probability of 7.8 were obtained. These experimental results indicate that the proposed technique is useful for CAD systems to detect non-melanoma skin lesions in dermoscopy images.

Keywords

Computer-Aided Diagnosis (CAD) Dermoscopy Non-melanoma skin lesions Contrast enhancement Segmentation Active contour 

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References

  1. 1.
    Perednia, D.A., Gaines, J.A., Rossum, A.C.: Variability in physician assessment of lesions in cutaneous images and its implications for skin screening and computer-assisted diagnosis. Arch. Dermatol. 128, 357–364 (1992)CrossRefGoogle Scholar
  2. 2.
    Abbas, Q., Emre Celebi, M., Fondón, I., Ahmad, W.: Melanoma recognition framework based on expert definition of ABCD for dermoscopic images, skin research and technology (2012) (in press)Google Scholar
  3. 3.
    Argenziano, G., Soyer, H.P., Chimenti, S., Talamini, R., Corona, R., Sera, F., et al.: Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J. Am. Acad. Dermatol. 48(5), 679–693 (2003)CrossRefGoogle Scholar
  4. 4.
    Ballerini, L., Fisher, R.B., Aldridge, B., Rees, J.: A color and texture based hierarchical k-nn approach to the classification of non-melanoma skin lesions. In: Color Medical Image Analysis. Springer (2012) (in press)Google Scholar
  5. 5.
    Ko, C.B., Walton, S., Keczkes, K., Bury, H.P.R., Nicholson, C.: The emerging epidemic of skin cancer. British Journal of Dermatology 130, 269–272 (1994)CrossRefGoogle Scholar
  6. 6.
    Celebi, M.E., Kingravi, H.A., Uddin, B., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imag. Grap. 31(6), 362–373 (2007)CrossRefGoogle Scholar
  7. 7.
    Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Imag. Grap. 33(3), 148–153 (2009)CrossRefGoogle Scholar
  8. 8.
    Emre Celebi, M., Wen, Q., Hwang, S., Iyatomi, H., Schaefer, G.: Lesion Border Detection in Dermoscopy Images Using Ensembles of Thresholding Methods. Skin Res. Technol. (2012) (in press)Google Scholar
  9. 9.
    Iyatomi, H., Oka, H., Celebi, M.E., et al.: An improved Internet-based melanoma screening system with Dermatologist-like tumor area extraction algorithm. Comput. Med. Imag. Grap. 32(7), 566–579 (2008)CrossRefGoogle Scholar
  10. 10.
    Gomez, D.D., Butakoff, C., Ersboll, B.K., Stoecker, W.V.: Independent histogram pursuit for segmentation of skin lesions. IEEE T. Biomed. Eng. 55(1), 157–161 (2008)CrossRefGoogle Scholar
  11. 11.
    Tang, J.: A multi-direction GVF snake for the segmentation of skin cancer images. Pattern Recogn. 42(6), 1172–1179 (2009)CrossRefGoogle Scholar
  12. 12.
    Yuan, X., Situ, N., Zouridakis, G.: A narrow band graph partitioning method for skin lesion segmentation. Pattern Recogn. 42(6), 1017–1028 (2009)CrossRefzbMATHGoogle Scholar
  13. 13.
    Abbas, Q., Fondón, I., Rashid, M.: Unsupervised skin lesions border detection via two-dimensional image analysis. Comput. Meth. Prog. Bio. (2010)Google Scholar
  14. 14.
    Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE T. Image Process. 17(11), 2029–2039 (2008)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Liu, W., Shang, Y., Yang, X.: Active contour model driven by local histogram fitting energy. Pattern Recognition Letters 34(6), 655–662 (2013)CrossRefGoogle Scholar
  16. 16.
    Fairchild, M.D.: A revision of CIECAM97s for practical applications. Color Research & Applications 26(6), 418–427 (2001)CrossRefGoogle Scholar
  17. 17.
    Seow, M.J., Asari, V.K.: Ratio rule and homomorphic filter for enhancement of digital colour image. Neurocomputing 69, 954–958 (2006)CrossRefGoogle Scholar
  18. 18.
    Argenziano, G., Soyer, P.H., De, V.G., Carli, P., Delfino, M.: Interactive atlas of dermoscopy CD. EDRA medical publishing and New media, Milan (2002)Google Scholar
  19. 19.
    Celebi, M.E., Aslandogan, A., Stoecker, W.V.: Unsupervised Border Detection in Dermoscopy Images. Skin Research and Technology 13(4), 454–462 (2007)CrossRefGoogle Scholar
  20. 20.
    Smith, J.R.: Color for image retrieval. In: Image Databases, ch. 11, pp. 285–311. John Wiley & Sons, Inc. (2002)Google Scholar
  21. 21.
    Huang, Z.-K., Liu, D.-H.: Segmentation of color image using EM algorithm in HSV color space. In: Proceedings of IEEE International Conference on Information Acquisition, pp. 316–319 (July 2007)Google Scholar
  22. 22.
    Chang, C., Chen, K., Wang, J., Althouse, M.L.G.: A Relative Entropy Based Approach in Image Thresholding. Pattern Recognition 27, 1275–1289 (1994)CrossRefGoogle Scholar
  23. 23.
    Melanocytic Lesions. Medical Image Analysis 7(1), 47–64 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qaisar Abbas
    • 1
    • 2
  • Irene Fondón
    • 3
    Email author
  • Auxiliadora Sarmiento
    • 3
  • M. Emre Celebi
    • 4
  1. 1.Department of Computer ScienceCOMSATS Institute of Information TechnologyIslamabadPakistan
  2. 2.College of Computer and Information SciencesAl-Imam Muhammad ibn Saud Islamic UniversityRiyadhSaudi Arabia
  3. 3.Signal Theory DepartamentUniversity of SevilleSevilleSpain
  4. 4.Department of Computer ScienceLouisiana State UniversityShreveportUSA

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