Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Security

ICAIS 2019: Artificial Intelligence and Security pp 391–401Cite as

Sparse Representation-Based Radiomics in the Diagnosis of Thyroid Nodules

  • Conference paper
  • First Online:

  • 1686 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11632))

Abstract

Thyroid nodules are a disease with a high clinical incidence, but only about 5% of thyroid nodules are malignant. If they are detected and treated early, they can usually be completely cured. The diagnosis of benign and malignant thyroid nodules is for treatment. The choice of method is of great significance, so it is of great significance to correctly judge the benign and malignant thyroid nodules. Radiosurgery is a non-invasive diagnostic technique based on quantitative medical image analysis. However, current radionomics techniques do not have uniform standards for feature extraction, feature selection, and prediction. In this paper, we propose a radionomics system based on sparse representation for the diagnosis of benign and malignant thyroid nodules. First, we developed a feature extraction method based on dictionary learning and sparse representation, which takes advantage of the statistical properties of lesion regions, compared with traditional feature methods based on specified features and specific directions. Feature extraction is more comprehensive and effective. Then, I used a principal component analysis to reduce the extracted thyroid nodule features, and combined the potentially high-dimensional variables into linearly independent low-dimensional variables. Finally, I used the Naive Bayes classifier to classify the data obtained by dimensionality reduction. The total accuracy of the model is 93.3%, the sensitivity is 85.1%, the specificity is 98.6%. The classification result AUC value based on the sparse representation method is 0.920. Experimental results show that the proposed method has a good classification result.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Li, W., Zhu, Q., Jiang, Y., et al.: Application of thin-layer liquid-based cytology smear in ultrasound-guided fine needle aspiration biopsy of thyroid. Union Med. J. 5(1), 8–12 (2014)

    Google Scholar 

  2. Zhang, T., Qu, N., Zheng, P., et al.: Machine learning in the diagnosis and treatment of thyroid tumors. Chin. J. Cancer and so on

    Google Scholar 

  3. Lambin, P., Rios-Velaquez, E., Leijienaar, R., et al.: Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48(4), 441–446 (2012)

    Article  Google Scholar 

  4. Zhuangtiango: From radiography to radioimaging histology - Commemorating the 120th anniversary of the discovery of X-rays by Rontgen. Adv. Biomed. Eng. 36(4), 189–195 (2015)

    Google Scholar 

  5. Yip, S., Aerts, H.: Applications and limitations of radiomics. Phys. Med. Biol. 61(13), R150–R166 (2016)

    Article  Google Scholar 

  6. Yu, J., et al.: Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur. Radiol. 27(8), 3509–3522 (2016)

    Article  Google Scholar 

  7. Aerts, H.J., et al.: Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci. Rep. 6, Art. No. 33860 (2016)

    Google Scholar 

  8. Teruel, J.R., et al.: Dynamic contrast-enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer. NMR Biomed. 27(8), 887–896 (2014)

    Article  Google Scholar 

  9. Kumar, V., et al.: Radiomics: the process and the challenges. Magn. Reson. Imag. 30(9), 1234 (2012)

    Article  Google Scholar 

  10. Das, S., Jena, U.R.: Texture classification using combination of LBP and GLRLM features along with KNN and multiclass SVM classification. In: Proceedings of IEEE Conference CCIS, Mathura, India, pp. 115–119, November 2016

    Google Scholar 

  11. Xu, L., Li, J., Shu, Y., Peng, J.: SAR image denoising via clustering based principal component analysis. IEEE Trans. Geosci. Remote Sens. 52(11), 6858–6869 (2014)

    Article  Google Scholar 

  12. Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., Chen, Z.: Magnetic resonance image reconstruction from undersampled measurements using a patch-based non-local operator. Med. Image Anal. 18(6), 843–856 (2014)

    Article  Google Scholar 

  13. Dong, W., Shi, G., Li, X., Ma, Y., Huang, F.: Compressive sensing via non-local low-rank regularization. IEEE Trans. Image Process. 23(8), 3618–3632 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  14. Dong, W., Zhang, L., Shi, G., Li, X.: Nonlocally centralized sparse representation for image restoration. IEEE Trans. Image Process. 22(4), 1620–1630 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  15. Rubinstein, R., Bruckstein, A.M., Elad, M.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045–1057 (2010)

    Article  Google Scholar 

  16. Dong, W., Zhang, D., Shi, G.: Centralized sparse representation for image restoration. In: Proceedings of IEEE Conference ICCV, pp. 1259–1266, November 2011

    Google Scholar 

  17. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  18. Hojjatoleslami, S., Kittler, J.: Region growing: a new approach. IEEE Trans. Image Process. 7(7), 1079–1084 (1998)

    Article  Google Scholar 

  19. Dehmeshki, J., Amin, H., Valdivieso, M., Ye, X.: Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE Trans. Med. Imaging 27(4), 467–480 (2008)

    Article  Google Scholar 

  20. Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd edn. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

  21. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)

    Article  Google Scholar 

  22. Ding, C., Huang, H., Yang, Y.: Description and classification of leather defects based on principal component analysis. J. Donghua Univ. (Engl. Ed.), 1–7 (2019)

    Google Scholar 

  23. Zhu, M.: Study on Bayesian Network Structure Learning and Reasoning. Xi’an University of Electronic Science and Technology (2013)

    Google Scholar 

  24. Gao, H., Chae, O.: Individual tooth segmentation from CT images using level set method with shape and intensity prior. Pattern Recogn. 43(7), 2406–2417 (2010)

    Article  Google Scholar 

  25. Chen, Y.T.: A level set method based on the Bayesian risk for medical image segmentation. Pattern Recogn. 43(11), 3699–3711 (2010)

    Article  MATH  Google Scholar 

  26. Krishnan, K., Ibanez, L., Turner, W.D., Jomier, J., Avila, R.S.: An opensource toolkit for the volumetric measurement of CT lung lesions. Opt. Express 18(14), 15256–15266 (2010)

    Article  Google Scholar 

  27. Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  28. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  29. So, R.W.K., Tang, T.W.H., Chung, A.: Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recogn. 44, 2450–2467 (2011)

    Article  Google Scholar 

  30. Xu, N., Bansal, R., Ahuja, N.: Object segmentation using graph cuts based active contours, vol. 42, pp. II-46–II53. IEEE (2003)

    Google Scholar 

  31. Slabaugh, G., Unal, G.: Graph cuts segmentation using an elliptical shape prior, pp. II-1222–II1225. IEEE (2005)

    Google Scholar 

  32. Liu, X., Veksler, O., Samarabandu, J.: Graph cut with ordering constraints on labels and its applications, pp. 1–8. IEEE (2008)

    Google Scholar 

  33. Ye, X., Beddoe, G., Slabaugh, G.: Automatic graph cut segmentation of lesions in CT using mean shift superpixels. J. Biomed. Imaging 2010, 19 (2010)

    Google Scholar 

  34. Liu, W., Zagzebski, J.A., Varghese, T., Dyer, C.R., Techavipoo, U., Hall, T.J.: Segmentation of elastographic images using a coarse-to-fine active contour model. Ultrasound Med. Biol. 32(3), 397–408 (2006)

    Article  Google Scholar 

  35. He, Q., Duan, Y., Miles, J., Takahashi, N.: A context-sensitive active contour for 2D corpus callosum segmentation. Int. J. Biomed. Imaging 2007(3), 24826 (2007)

    Google Scholar 

  36. Chen, C., Li, H., Zhou, X., Wong, S.: Constraint factor graph cut–based active contour method for automated cellular image segmentation in RNAi screening. J. Microsc. 230(2), 177–191 (2008)

    Article  MathSciNet  Google Scholar 

  37. Suzuki, K., Kohlbrenner, R., Epstein, M.L., Obajuluwa, A.M., Xu, J., Hori, M.: Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms. Med. Phys. 37, 2159 (2010)

    Article  Google Scholar 

  38. Wang, L., Li, C., Sun, Q., Xia, D., Kao, C.Y.: Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation. Comput. Med. Imaging Graph 33(7), 520–531 (2009)

    Article  Google Scholar 

  39. Wu, Q., Li, Y., Lin, Y., Zhou, R.: Weighted sparse image classification based on low rank representation. CMC Comput. Mater. Contin. 56(1), 91–105 (2018)

    Google Scholar 

  40. Wang, R., Shen, M., Li, Y., Gomes, S.: Multi-task Joint sparse representation classification based on fisher discrimination dictionary learning. CMC Comput. Mater. Contin. 57(1), 25–48 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China

    Yunhua Cao & Guang Yang

  2. Collaborative Innovation Center for Image and Geospatial Information, Chengdu University of Information and Technology, Chengdu, 610225, China

    Ying Fu

Authors
  1. Yunhua Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ying Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Fu .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cao, Y., Fu, Y., Yang, G. (2019). Sparse Representation-Based Radiomics in the Diagnosis of Thyroid Nodules. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24274-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24273-2

  • Online ISBN: 978-3-030-24274-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation