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Dermoscopy Image Processing for Chinese

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Book cover Computer Vision Techniques for the Diagnosis of Skin Cancer

Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

Dermoscopy image analysis technology is discussed based on Chinese in this chapter. It includes four aspects: preprocessing, segmentation, feature extraction and classification. Firstly, in preprocessing stage, hair is extracted out according to the elongate of connected region, and then removed from the image by using PDE-based image inpainting technology. Secondly, a novel dermoscopy image segmentation algorithm is introduced using self-generating neural network (SGNN) combined with genetic algorithm (GA). And in the feature description stage, the features including color, texture, shape and border are extracted for the lesion object. Lastly, the model of combined neural network classifier is employed to classify the lesion object successfully with a sensitivity and specificity of 93.3 and 96.7 % respectively. Based on the image analysis method disscussed in this chapter, an automatic analysis system of dermoscopy images of Chinese is successfully developed and has been applied for the clinical diagnosis of skin tumors.

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References

  1. Siegel, R., Ward, E., Brawley, O., Jemal, A.: Cancer statistics. CA Cancer J. Clin. 61(4), 212–236 (2011)

    Article  Google Scholar 

  2. Korotkov, K., Garcia, R.: Computerized analysis of pigmented skin lesions: a review. Artif. Intell. Med. 56(2), 69–90 (2012)

    Article  Google Scholar 

  3. Celebi, M.E., Stoecker, W.V., Moss, R.H.: advances in skin cancer image analysis. Comput. Med. Imaging Graph. 35(2), 83–84 (2011)

    Article  Google Scholar 

  4. Andreassi, L., Pemtti, R., Burroni, M.: Computerized image analysis of pigmented lesions. Chronica Dermatol 1, 11–24 (1995)

    Google Scholar 

  5. Slue, W., Kopf, A.W., Rivers, J.K.: Total body photographs of dysplastic nevi. Archives of Dermatology 124, 1239–1243 (1988)

    Article  Google Scholar 

  6. Lee, T.K., Ng, V., Gallagher, R., et al.: Dullrazor: a software approach to hair removal from images. Comput. Biol. Med. 27(6), 533–543 (1997)

    Article  Google Scholar 

  7. Wighton, P., Lee, T.K., AtkinsaM.S.: Dermoscopic hair disocclusion using inpainting. In: Proceedings of the SPIE medical imaging, vol. 6914, pp. 691427–691427-8 (2008)

    Google Scholar 

  8. Zhou, H., Chen, M., Gass, R., Rehg, J.M., Ferris, L., Ho J., et al.: Feature-preserving artifact removal from dermoscopy images. In: Proceedings of the SPIE medical imaging, vol. 6914, pp. 69141B–69141B-9 (2008)

    Google Scholar 

  9. Kiani, K., Sharafat, A.R., Shaver, E., etal.: An improved dullrazor for digitally removing dark and light-colored hairs in dermoscopic images. Comput. Biol. Med. 41(3), 139–145 (2011)

    Google Scholar 

  10. Abbas, Q., Celebi, M.E., Garcia, I.F.: Hair removal methods: a comparative study for dermoscopy images. Biomed. Signal Process. Control 6(4), 395–404 (2011)

    Article  Google Scholar 

  11. Abbas, Q., Garcia, I.F., Celebi, M.E., Ahmad, W.: A feature-preserving hair removal algorithm for dermoscopy images. Skin Res. Techhnol. 19(1), e27–e36 (2013)

    Article  Google Scholar 

  12. Xie, F.Y., Qin, S.Y., Jiang, Z.G., Meng, R.S.: PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Comput. Med. Imaging Graph. 33(4), 275–282 (2009)

    Article  Google Scholar 

  13. Zeng, M., Li, J.X.: Optimized design of morphological improved top-hat filter based on improved genetic algorithms. Acta Optica Sinica 26(4), 510–515 (2006)

    Google Scholar 

  14. Soille, P.: Morphological Image Analysis: Principles and Applications, Springer, Berlin (1999)

    Google Scholar 

  15. Cui, Y.: Mathematics morphological algorithms and its application. Science Press, Beijing, China (2000)

    Google Scholar 

  16. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  17. Celebi, M.E., Kingravi, H., Iyatomi, H., Aslandogan, A., Stoecker, W.V., Moss, R.H.: Border detection in dermoscopy images using statistical region merging. Skin Res. and Technol. 14(3), 347–353 (2008)

    Article  Google Scholar 

  18. Celebi, M.E., Iyatomi, H., Schaefer, G., Stoecker, W.V.: Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 33(2), 148–153 (2009)

    Article  Google Scholar 

  19. Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.: A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions. IEEE Trans. Med. Imaging 22(8), 959–964 (2003)

    Article  Google Scholar 

  20. Rubegni, P., Ferrari, A., Cevenini, G., Piccolo, D., Burron, M., et al.: Differentiation between pigmented spitz naevus and melanoma by digital dermoscopy and stepwise logistic discriminant analysis. Melanoma Res. 11(1), 37–44 (2011)

    Article  Google Scholar 

  21. Zhou, H., Schaefer, G., Celebi, M.E., Lin, F., Liu, T.: Gradient Vector Flow with Mean Shift for Skin Lesion Segmentation. Comput. Med. Imaging Graph. 35(2), 121–127 (2011)

    Article  Google Scholar 

  22. Celebi, M.E., Aslandogan, A., Stoecker, W.V.: Unsupervised border detection in dermoscopy images. Skin Res. Technol. 13(4), 454–462 (2007)

    Article  Google Scholar 

  23. Zhou, H., Schaefer, G., Sadka, A.H., Celebi, M.E.: Anisotropic mean shift based fuzzy c-means segmentation of dermoscopy images. IEEE J. Sel. Top. Sign. Proces. 3(1), 26–34 (2009)

    Google Scholar 

  24. Gao, J., Zhang, J., Fleming, M.G.: A novel multiresolution color image segmentation technique and its application to dermatoscopic image segmentation. In: Proceeding of IEEE International Conference on Image Process, Vancouver, BC, Canada (2000)

    Google Scholar 

  25. Cucchiara, R., Grana, C., Seidenari, S., Pellacani, G.: Exploiting color and topological features for region segmentation with recursive fuzzy C-means. Machine Graphics Vision 11(2/3), 169–182 (2002)

    Google Scholar 

  26. Xie, F., Bovik, Al.: Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm. Pattern Recognit. 46(3), 1012–1019 (2013)

    Google Scholar 

  27. Wen, W.X., Jennings, A., Liu, H.: Learning a neural tree. In: Proceeding of International Joint Conference on Neural Networks, vol. 2, pp. 751–756, Beijing, China (1992)

    Google Scholar 

  28. Inoue, H., Narihisa, H.: Efficiency of self-generating neural networks applied to pattern recognition. Math. Comput. Model. 38(11–13), 1225–1232 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  29. Feng, S., Chaudhari, N.S.: A chaotic behavior decision algorithm based on self-generating neural network for computer games. In: Proceedings of IEEE Conference on 3rd Industrial Electronics and Applications, pp. 1912–1915, Singapore (2008)

    Google Scholar 

  30. Inoue, H., Narihisa, H.: Efficient pruning method for ensemble self-generating nerual networks. J. Syst. Cybern. Inf. 1(6), 72–77 (2003)

    Google Scholar 

  31. Mukhopadhyay, A., Bandyopadhyay, S., Maulik U.l.: Clustering using Multi-objective genetic algorithm and its application to image segmentation. In: Proceeding of IEEE International Conference on Systems, Man, and Cybernetics, vol. 10, pp. 2678–2683 (2006)

    Google Scholar 

  32. Zhu, Y., Jiang, L.J.: Image Segmentation Based on GA-FCM Clustering and Probability Relaxation. Laser Infrared 38(4), 292–295 (2008)

    Google Scholar 

  33. Awad, M., Chehdi, K., Nasri, A.: Multicomponent image segmentation using a genetic algorithm and artificial neural network. IEEE Geosci. Remote Sens. Lett. 4(4), 571–575 (2007)

    Article  Google Scholar 

  34. Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Trans. Evolut Ccmput. 3(2), 103–112 (1999)

    Article  Google Scholar 

  35. Maulik, U., Bandyopadhyay, S.: Genetic algorithm based clustering technique. Pattern Recogn. 33, 1455–1465 (2000)

    Article  Google Scholar 

  36. Maulik, U., Bandyopadhyay, S.: Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification. IEEE Trans. Geosci. Remote Sens. 41(5), 1075–1081 (2003)

    Article  Google Scholar 

  37. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. B Cybern. 9(1), 62–66 (1979)

    Article  MathSciNet  Google Scholar 

  38. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 1, 224–227 (1979)

    Article  Google Scholar 

  39. Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974)

    Article  MathSciNet  Google Scholar 

  40. Halkidi, M., Vazirgiannis, M., Batistakis, Y.: Quality scheme assessment in the clustering process. In: Procedding of European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 265–276, Lyon, France (2000)

    Google Scholar 

  41. Tanaka, T., Yamada, R., Tanaka, M., et al.: A study on the image diagnosis of melanoma. Proceedings of the 26th Annual International Conference of the IEEE EMBS, vol. 9, pp. 1597–1600 (2004)

    Google Scholar 

  42. Motoyarna, H., Tanaka, T., Tanka, M., et al.: Feature of malignant melanoma based on color information. SICE Annual Conf. Sapporo 1, 230–233 (2004)

    Google Scholar 

  43. Sachin, V., Atam, P.: Multi-spectral imaging and analysis for classification of melanoma. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS vol. 9, pp. 503–506 (2004)

    Google Scholar 

  44. Celebi, M.E., Kingravi, H.A., Uddin, B., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31, 362–373 (2007)

    Article  Google Scholar 

  45. Abbas, Q., Celebi, M.E., Serrano, C., García, I.F.: Pattern classification of dermoscopy images: A perceptually uniform model. Pattern recogn. 46(1), 86–97 (2013)

    Article  Google Scholar 

  46. Ganster, H., Pinz, A., Rohrer, R., et al.: Automated melanoma recognition. IEEE Trans. Med. Imaging 20(3), 233–239 (2001)

    Article  Google Scholar 

  47. Hintz-Madsen M., Hansen L. K., Larsen J., et al. A probabilistic neural network framework for detection of malignant melanoma. In: Naguib RNG, Sherbet GV(ed) Artificial Neural Networks in Cancer Diagnosis, Prognosis, and Patient Management, CRC Press, Boca Raton, 141–183 (2001)

    Google Scholar 

  48. Rubegni, P., Burroni, M., Cevenini, G., et al.: Digital dermoscopy. Analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: A retrospective study. J. Invest. Dermatol. 119(2), 471–474 (2002)

    Article  Google Scholar 

  49. Sboner, A., Eccher, C., Blanzieri, E., et al.: A multiple classifier system for early melanoma diagnosis. Artif. Intell. Med. 27(1), 29–44 (2003)

    Article  Google Scholar 

  50. Blum, A., Luedtke, H., Ellwanger, U., et al.: Digital image analysis for diagnosis of cutaneous melanoma development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Br. J. Dermatol. 151(5), 1029–1038 (2004)

    Article  Google Scholar 

  51. Menzies, S.W., Bischof, L., Talbot, H., et al.: The performance of SolarScan: An automated dermoscopy image analysis instrument for the diagnosis of primary melanoma. Arch. Dermatol. 141(11), 1388–1396 (2005)

    Article  Google Scholar 

  52. Capdehourat, G., Corez, A., Bazzano, A., et al.: pigmented skin lesions classification using dermatoscopic images. Progress Pattern Recognition, Image Analysis, Computer Vision, Applications 5856(11), 537–544 (2009)

    Article  Google Scholar 

  53. Stolz, W., Riemann, A., Cognetta, A.B., et al.: ABCD rules of dermatoscopy: a new practical method for early recognition of malignant melanoma. Eur. J. Dermatol. 4(7), 521–527 (1994)

    Google Scholar 

  54. Menzies, S., Crook, B., McCarthy, W., et al.: Automated instrumentation and diagnosis of invasive melanoma. Melanoma Res. 7(Suppl. 1), s13 (1997)

    Article  Google Scholar 

  55. McGovern, T.W., Litaker, M.S.: Clinical predictors of malignant pigmented lesions: a comparson of the Glasgow seven-point checklist and the American Cancer Society’s ABCDs of pigmented lesions. J. Dermatol. Surg. Onc 18, 22–26 (1992)

    Article  Google Scholar 

  56. Celebi, M.E., Kingravi, H.A., Uddin, B., et al.: A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31, 362–373 (2007)

    Article  Google Scholar 

  57. Fengying Xie. (2009) Segmentation and Classification of Dermoscopy Images Based on Computational Intelligence.doctor dissertation at Beihang University in China.

    Google Scholar 

  58. Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of gray level quantization. Can. J. Remote Sens. 28(1), 45–62 (2002)

    Article  Google Scholar 

  59. Andrew, A.M.: Another Efficient Algorithm for Convex Hulls in Two Dimensions. Inf. Process. Lett. 9(5), 216–219 (1979)

    Article  MATH  Google Scholar 

  60. Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990)

    Article  Google Scholar 

  61. Hashem, S., Schmeiser, B., Yih Y.:Optimal linear combinations of neural networks: An Overview. In: IEEE International Conference on Neyral Networks pp. 93–19 (1994)

    Google Scholar 

  62. Freund, Y., Schapire, R.E.: A Decision-TheoreticGeneralization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  63. Shen, Z.Q., Kong, F.S.: Optimizing weights by genetic algorithm for neural network ensemble. Lect. Notes Comput. Sci. 323–331 (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of astronautics, Beihang University, Beijing, 100191, China

    Fengying Xie, Yefen Wu & Zhiguo Jiang

  2. Department of Dermatology, General Hospital of the Air Force of the Chinese People’s Liberation Army, Beijing, 100142, China

    Rusong Meng

Authors
  1. Fengying Xie
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  2. Yefen Wu
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  3. Zhiguo Jiang
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  4. Rusong Meng
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Corresponding author

Correspondence to Fengying Xie .

Editor information

Editors and Affiliations

  1. Federal University of Rio Grande do Sul, Porto Alegre, Brazil

    Jacob Scharcanski

  2. Computer Science dept., Louisiana State University Shreveport, Shreveport, Louisiana, USA

    M. Emre Celebi

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Xie, F., Wu, Y., Jiang, Z., Meng, R. (2014). Dermoscopy Image Processing for Chinese. In: Scharcanski, J., Celebi, M. (eds) Computer Vision Techniques for the Diagnosis of Skin Cancer. Series in BioEngineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39608-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-39608-3_5

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