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Fully Convolutional-based Dense Network for Lung Nodule Image Retrieval Algorithm

Volume 15, Number 1, January 2019, pp. 326-336
DOI: 10.23940/ijpe.19.01.p33.326336

Pinle Qina,b, Qi Lia,b, Jianchao Zenga,b, Haiyan Liuc, and Yuhao Cuia

aSchool of Data Science and Technology, North University of China, Taiyuan, 030051, China
bShanxi Provincial Key Laboratory for Biomedical Imaging and Big Data, Taiyuan, 030051, China
cFirst Hospital of Shanxi Medical University, Taiyuan, 030051, China

(Submitted on October 20, 2018; Revised on November 21, 2018; Accepted on December 23, 2018)

Abstract:

At present, there are many problems in the existing content-based medical image retrieval (CBMIR) algorithms. The most important problem is the lack of feature extraction, resulting in the imperfect expression of semantic information and the lack of data-based learning ability. Meanwhile, the characteristic dimension is high, which affects the performance of image retrieval. In order to solve these problems, this paper presents a fully convolutional dense network (FCDN) algorithm, which solves the gap between the extracted low-level features and high-level semantic features. In order to improve the accuracy and efficiency of retrieval, the concept of Joint distance is proposed in this paper. Since the image information of lung nodules extracted from different layers of the network is different, the minimum Joint distance is selected by comparing the minimum Hamming distances of the layers 4, 17 and 25 of the similar images retrieved. Compared with other methods, the average accuracy of the lung nodule image retrieval can reach 91.17% under the 64-bit hash code length, the average time for retrieving a lung slice is 4.8×10-5 s, The search results not only express the rich semantic features of the image, but also improve the retrieval efficiency. And the retrieval performance is better than other network structures to help doctors assist in diagnosis.

 

References: 20

      1. A. Qayyum, S. M. Anwar, M. Awais, and M. Majid, “Medical Image Retrieval using Deep Convolutional Neural Network,” Neurocomputing, Vol. 266, No. 29, pp. 8-20, November 2017
      2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” in Proceedings of the International Conference on Neural Information Processing Systems, pp. 1097-1105, Nevada, December 2012
      3. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv Preprint arXiv: 1409.1556, September 2014
      4. Y. Chen, X. Jin, B. Kang, J. Feng, and S. Yan, “Sharing Residual Units Through Collective Tensor Factorization in Deep Neural Networks,” in Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-10, Honolulu, Hawaii, USA, March 2017
      5. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, Las Vegas, USA, June 2016
      6. J. Kim, J. K. Lee, and K. M. Lee, “Accurate Image Super-Resolution using Very Deep Convolutional Networks,” in Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1646-1654, Las Vegas, USA, June 2016
      7. D. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, Vol. 60, No. 2, pp. 91-110, November 2004
      8. G. Huang, Z. Liu, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, Honolulu, Hawaii, USA, July 2017
      9. A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images,” University of Toronto, 2009
      10. J. Deng, W. Dong, R. Socher, L. J. Li, and K. Li, “ImageNet: A Large-Scale Hierarchical Image Database,” in Proceedings of the 2005 Computer Vision and Pattern Recognition, pp. 248-255, Miami, Florida, USA, June 2009
      11. N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” in Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 886-893, San Diego, CA, USA, June 2005
      12. K. He, X. Zhang, S. Ren, and J. Sun, “Identity Mappings in Deep Residual Networks,” in Proceedings of the 2016 European Conference on Computer Vision, pp. 630-645, Amsterdam, The Netherlands, March 2016
      13. X. Lu, L. Song, R. Xie, X. Yang, and W. Zhang, “Deep Hash Learning for Efficient Image Retrieval,” in Proceedings of 2017 IEEE International Conference on Multimedia & Expo Workshops IEEE Computer Society, pp. 579-584, Hong Kong, July 2017
      14. S. Conjeti , A. G. Roy, A. Katouzian , and N. Navab, “Deep Residual Hashing,” arXiv: 1612.05400, December 2016
      15. L. Duan, C. Zhao, J. Miao, Y. Qiao, and X. Su, “Deep Hashing based Fusing Index Method for Large-Scale Image Retrieval,” Applied Computational Intelligence and Soft Computing, Vol. 2017, pp.1-8, May 2017
      16. G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Q. Weinberger, “Deep Networks with Stochastic Depth,” in Proceedings of the 2016 European Conference on Computer Vision (ECCV), pp. 646-661, Amsterdam, Netherlands, October 2016
      17. A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, “Cnn Features off-the-Shelf: An Astounding Baseline for Recognition,” in Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 512-519, June 2014
      18. R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based Convolutional Networks for Accurate Object Detection and Semantic Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, No. 1, pp. 142-158, January 2016
      19. S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,” arXiv: 1502.03167, February 2015
      20. X. Yang, Q. Yan, and J. Zhao, “Hashing Retrieval for CT Images of Pulmonary Nodules based on Medical Signs and Convolutional Neural Networks,” CAAI Transactions on Intelligent Systems, Vol. 12, No. 6, pp. 857-864, December 2017

           

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