Advertisement

Deep Semantics-Preserving Hashing Based Skin Lesion Image Retrieval

  • Xiaorong PuEmail author
  • Yan Li
  • Hang Qiu
  • Yinhui Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10262)

Abstract

This study proposes a content-based pigmented skin lesion image retrieval scheme on semantic hash clustering on the output of the deep neural networks. The skin lesion images are acquired with standard digital cameras or mobile phones. To retrieval skin lesion images efficiently online, semi-supervised deep convolutional neural network incorporated with hash functions jointly learn feature representations, for preserving similar semantics between skin lesion images, and mappings to hash codes. The target candidates are clustered by Affinity Propagation (AP) for ranking, which are selected among the outputs of layer F7 based on the Hamming distance of their semantic hash codes. Experiments on 4 disease categories of pigmented skin lesions of a set of 239 images yielded a specificity of 93.4% and a sensitivity of 80.89%.

Keywords

Semantics hash coding Pigmented skin lesion Image retrieval Affinity propagation cluster 

References

  1. 1.
    Nilkamal, S.R., Shweta, V.J.: ABCD rule based automatic computer-aided skin cancer detection using MATLAB®. Int. J. Comput. Technol. Appl. 4(4), 691–719 (2013)Google Scholar
  2. 2.
    Pu, X.R., Wu, X.J., Ouyang, K.M., Ji, L.P.: Automatic hair removal for skin lesion images from regular digital cameras. In: 2015 International Symposium on Electrical, Electronic Engineering and Digital Technology, pp. 714–721 (2015)Google Scholar
  3. 3.
    Cavalcanti, P., Yari, Y., Scharcanski, J.: Pigmented skin lesion segmentation on macroscopic images. In: The 25th International Conference on Image and Vision Computing, pp. 1–7 (2010)Google Scholar
  4. 4.
    Zhao, F., Huang, Y., Wang, L., Tan, T.N.: Deep semantic ranking based hashing for multi-label image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1556–1564 (2015)Google Scholar
  5. 5.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  6. 6.
    Chen, Y., Wang, J.Z., Krovetz, R.: CLUE: cluster-based retrieval of images by unsupervised learning. IEEE Trans. Image Process. 14(8), 1187–1201 (2005)CrossRefGoogle Scholar
  7. 7.
    Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Wang, J., Kumar, S., Chang, S.F.: Semi-supervised hashing for scalable image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3424–3431 (2010)Google Scholar
  9. 9.
    Girshick, R., Donahue, J., Darrell, T., Malik, Z.J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  10. 10.
    Wang, J., Song, Y., Leung, T., Rosenberg, C., Wu, Y.: Learning fine-grained image similarity with deep ranking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1386–1393 (2012)Google Scholar
  11. 11.
    Yang, H.F., Lin, K., Chen, C.S.: Supervised learning of semantics-preserving hashing via deep neural networks for large-scale image search. Computer Science (2015)Google Scholar
  12. 12.
    Henning, M., Nicolas, M., David, B., Antoine, G.: A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)CrossRefGoogle Scholar
  13. 13.
    Jia, Y., Shelhamer, E., Donahue, J., et al.: Caffe: convolutional architecture for fast feature embedding. In: The ACM International Conference on Multimedia, pp. 675–678 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Provincial Key Laboratory of Digital Media, Health Big Data Science Research Center, Big Data Research Center, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

Personalised recommendations