Abstract
Content based image retrieval (CBIR) that searches for similar images in a large database has been attracting increasing research interest recently, and it has been applied to medical image characterization for sharing experts’ experiences. One challenging task in CBIR is to extract features for effective image representation. To this end, bag-of-visual-words (BoVW) has been proven to be effective to extract middle-level features for image analysis. However, it is necessary to first vectorize the two- or three-dimensional spatial structure for analysis in conventional BoVW and then destroy the spatial relationships of nearby voxels. In this study, we propose a tensor sparse coding method, which is a multilinear generalization of conventional sparse coding (soft assignment in BoVW), to learn features from multi-dimensional medical images. We regard high-dimensional local structures as tensors and propose a K-CP (CANDECOMP/PARAFAC) algorithm to learn an overcomplete tensor dictionary iteratively. By using the learned overcomplete tensor dictionary, sparse coefficients of tensor local structures are calculated by employing the tensor orthogonal matching pursuit (Tensor-OMP) algorithm, which is an extended multilinear version of the conventional vector-based OMP. The proposed method is applied to the retrieval of focal liver lesions (FLLs) by using a medical database consisting of contrast-enhanced multi-phase computer-tomography (CT) images. Experiments show that the proposed tensor sparse coding method achieved better retrieval performance than conventional methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mir, A.H., Hanmandlu, M., Tandon, S.N.: Texture analysis of CT-images. IEEE Eng. Med. Biol. 5, 781–786 (1995)
Duda, D., Kretowski, M., Bezy-Wendling, J.: Texture characterization for hepatic tumor recognition in multiphase CT. Biocybern. Biomed. Eng. 26(4), 15–24 (2006)
Roy, S., Chi, Y., Liu, J., Venkatesh, S.K., Brown, M.S.: Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions. IEEE Trans. Biomed. Eng. 61(11), 2768–2778 (2014)
Xu, Y., Lin, L., Hu, H., Yu, H., Jin, C., Wang, J., Han, X.-H., Chen, Y.-W.: Combined density, texture and shape features of multi-phase contrast-enhanced ct images for cbir of focal liver lesions: a preliminary study. In: Innovation in Medicine and Healthcare 2015. Kyoto, Japan (2015)
Yang, W., Lu, Z., Yu, M., Huang, M., Feng, Q., Chen, W.: Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single- and multi-phase contrast-enhanced ct images. J. Digit. Imaging 25, 708–719 (2012)
Yu, M., Feng, Q., Yang, W., Gao, Y., Chen, W.: Extraction of lesion- partitioned features and retrieval of contrast-enhanced liver images. Comput. Math. Methods Med. (2012)
Diamant, I., Hoogi, A., Beaulieu, C.F., Safdari, M., Klang, E., Amitai, M., Greenspan, H., Rubin, D.L.: Improved patch based automated liver lesion classification by separate analysis of the interior and boundary regions. IEEE J. Biomed. Health Inf. 20(6), 1585–1594 (2016)
Xu, Y., Lin, L., Hu, H., Wang, D., Liu, Y., Wang, J., Chen, Y.-W., Han, X.: Bag of temporal co-occurrence words for retrieval of focal liverlesions using 3d multiphase contrast-enhanced CT images. In: 2016 23rd International Conference on Pattern Recognition (ICPR 2016) (2016)
Diamant, I., Klang, E., Amitai, M., Konen, E., Goldberger, J., Greenspan, H.: Task-driven dictionary learning based on mutual information for medical image classification. IEEE Trans. Biomed. Eng. 64(6), 1380–1392 (2017)
Wang, J., Han, X.H., Xu, Y., Lin, L., Hu, H., Jin, C., Chen, Y.W.: Sparse codebook model of local structures for retrieval of focal liver lesions using multi-phase medical images. Int. J. Biomed. Imaging 13pp. (2017). Article ID 1413297
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Acknowledgements
This research was supported in part by Shandong Provincial Natural Science Foundation under the Grant No. ZR2019BF035, and in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 18H03267, in part by Zhejiang Lab Program under the Grant No. 2020ND8AD01.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, J. et al. (2021). Automated Retrieval of Focal Liver Lesions in Multi-phase CT Images Using Tensor Sparse Representation. In: Chen, YW., Tanaka, S., Howlett, R.J., Jain, L.C. (eds) Innovation in Medicine and Healthcare. Smart Innovation, Systems and Technologies, vol 242. Springer, Singapore. https://doi.org/10.1007/978-981-16-3013-2_18
Download citation
DOI: https://doi.org/10.1007/978-981-16-3013-2_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3012-5
Online ISBN: 978-981-16-3013-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)