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Proceedings Paper

Deep transfer learning for MR image feature point descriptors
Author(s): Jia Chen; Haiyang Jiang; Ruhan He; Xinrong Hu; Junping Liu
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Paper 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.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117936 (14 August 2019); doi: 10.1117/12.2539665
Show Author Affiliations
Jia Chen, Wuhan Textile Univ. (China)
Haiyang Jiang, Wuhan Textile Univ. (China)
Ruhan He, Wuhan Textile Univ. (China)
Xinrong Hu, Wuhan Textile Univ. (China)
Junping Liu, Wuhan Textile Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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