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An Image Retrieval Method Based on Color and Texture Features for Dermoscopy Images

  • Xuedong Song
  • Fengying Xie
  • Jie Liu
  • Chang Shu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 875)

Abstract

Dermoscopy image retrieval can assist dermatologists to make a diagnosis by reference to confirmed cases, which can improve the accuracy of the diagnosis result. This paper proposed a retrieve method based on the combination of color and texture. The proposed method uses the color moments and Gabor wavelet to extract features and implements retrieval function by SKLSH hash code. In the experiments stage, we retrieve dermoscopy images including 4 kinds of skin diseases from the datasets which are pigmented nevus, seborrheic keratosis, psoriasis and eczema. Besides, we compared our methods with other color and texture features, as well as other dermoscopy image retrieval method, and the results show that our method obtains the best retrieval result.

Keywords

Dermoscopy image Image retrieve Computer-aided diagnosis Color feature Texture feature 

References

  1. 1.
    Rahman, M.M., Desai, B.C., Bhattacharya, P.: Image retrieval-based decision support system for dermatoscopic images. In: Computer Society, pp. 285–290 (2006)Google Scholar
  2. 2.
    Sabbaghi, S., Aldeen, M., et al.: A deep bag-of-features model for the classification of melanomas in dermoscopy images. Eng. Med. Biol. Soc. 16–20 (2016)Google Scholar
  3. 3.
    Menizies, S., Crotty, K., McCarthy, W., et al.: An Atlas of Surface Micorscopy of Pigmented Skin Lesion: Dermoscopy, 2nd edn. The McGraw-Hill Companies Inc., New York (2003)Google Scholar
  4. 4.
    Zhou, H., Xie, F., Jiang, Z., et al.: Multi-classification of skin diseases for dermoscopy images using deep learning. Imaging Syst. Tech. 1–5 (2017)Google Scholar
  5. 5.
    Sadri, A.R., Zekri, M., et al.: Segmentation of dermoscopy images using wavelet networks. Bio-med. Eng. 60, 1131–1141 (2012)Google Scholar
  6. 6.
    Yang, X., Zen, Z., Yeo, S.Y., et al.: A novel multi-task deep learning model for skin lesion segmentation and classification. In: Computer Vision and Pattern Recognition, pp. 1025–1028 (2017)Google Scholar
  7. 7.
    Cheng, Y.I., Swamisai, R., Umbaugh, S.E., et al.: Skin lesion classification using relative color features. Skin Res. Technol. 14, 53–64 (2008)Google Scholar
  8. 8.
    Esteva, A., Kuprel, B., Novoa, R.A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)CrossRefGoogle Scholar
  9. 9.
    Baldi, A., Murace, R., Dragonetti, E., et al.: CBIR system for dermoscopy images. Bio-med. Eng. 8, 8–18 (2009)Google Scholar
  10. 10.
    Sun, Y.: Research of Processing and Content-Retrieval Based on the Images of Pigmented Skin Lesions. University of Electronic Science and Technology of China, Chengdu, Sichuan, China (2016)Google Scholar
  11. 11.
    Liu, Y., Zhang, D., et al.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40, 262–282 (2007)CrossRefGoogle Scholar
  12. 12.
    Stricker, M., Orengo, M.: Similarity of color images. In: SPIE Proceedings, pp. 381–392 (1995)Google Scholar
  13. 13.
    Nurhadiyatna, A., Latifah, A.L., et al.: Gabor filtering for feature extraction in real time vehicle classification system. In: International Symposium on Image and Signal Processing and Analysis, pp. 19–24 (2015)Google Scholar
  14. 14.
    Maithiili, K., Elakkiy, P., et al.: Content based image retrieval with hash codes. Int. J. Adv. Res. Comput. Eng. Technol. 4, 1292–1295 (2015)Google Scholar
  15. 15.
    Raginsky, M., Lazebnik, S., et al.: Locality-sensitive binary codes from shift-invariant kernels. In: Neural Information Processing Systems, pp. 1509–1517 (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xuedong Song
    • 1
  • Fengying Xie
    • 1
  • Jie Liu
    • 2
  • Chang Shu
    • 2
  1. 1.Beijing Advanced Innovation Center for Biomedical Engineering, Image Processing CenterBeihang UniversityBeijingChina
  2. 2.Department of DermatologyPeking Union Medical College HospitalBeijingChina

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