Deep transfer learning for MR image feature point descriptors

Jia Chen; Haiyang Jiang; Ruhan He; Xinrong Hu; Junping Liu

doi:10.1117/12.2539665

14 August 2019 Deep transfer learning for MR image feature point descriptors

Jia Chen, Haiyang Jiang, Ruhan He, Xinrong Hu, Junping Liu

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117936 (2019) https://doi.org/10.1117/12.2539665
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

Abstract

In order to solve the internal feature matching problem of nonlinear flexible biological tissues in MR images, This paper proposes a feature point descriptor generation model based on transfer learning and convolutional neural networks TBNet . Firstly, the Siamese network structure model is combined with transfer learning to obtain a pre-trained CNN model and then this paper proposes a batch-by-batch model fine-tuning strategy. Secondly, the extracted feature point descriptor is obtained using the fine-tuned model. Finally, Experiments show that the TBNet has higher robustness and accuracy than traditional SIFT, SURF and the state-of-the-art VGG16-based models.

Citation Download Citation

Jia Chen, Haiyang Jiang, Ruhan He, Xinrong Hu, and Junping Liu "Deep transfer learning for MR image feature point descriptors", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117936 (14 August 2019); https://doi.org/10.1117/12.2539665

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