Skip to main content
SpringerLink
Log in

Content-based medical image retrieval of CT images of liver lesions using manifold learning

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

Accurate retrieval of liver CT images can help a specialist to decide on the type of lesion and treatment planning. However, the complex texture of the abnormality and its nonlinear characteristic reduces the recognition rate of a retrieval system. In this paper, we propose how to represent an abnormal region of a liver by individual attributes of a multi-phase CT image. The indexing of a medical image database is represented by a correlation graph distance, which considers nonlinear behavior of the feature space as well. The results showed that the average recall was improved by 7.5% using the proposed feature vector. Concerning a complex scheme for lesion representation and the manifold indexing technique, the recall of the system was increased by twice. The proposed indexing and feature representation prove the potential of our method in content-based medical image retrieval systems.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Golchin E, Maghooli K (2014) Overview of manifold learning and its application in medical data set. Int J Biomed Eng Sci (IJBES) 1(2):23–33

    Google Scholar 

  2. Li Z et al (2018) Large-scale retrieval for medical image analytics: a comprehensive review. Med Image Anal 43:66–84

    Article  Google Scholar 

  3. Pedronette DCG, Torres RS (2017) Unsupervised rank diffusion for content-based image retrieval. Neurocomputing 260:478–489

    Article  Google Scholar 

  4. Heidari H, Chalechale A, Mohammadabadi AA (2013) Parallel implementation of color based image retrieval using CUDA on the GPU. Int J Inf Technol Comput Sci (IJITCS) 6(1):33

    Google Scholar 

  5. Zin NAM, Yusof R, Lashari SA, Mustapha A, Senan N, Ibrahim R (2018) Content-based image retrieval in medical domain: a review. In: journal of physics: conference series, vol. 1019, no. 1, p. 012044. IOP Publishing

  6. Datta R et al (2008) Image retrieval: ideas, influences, and trends of the new age. ACM Comput Surv (Csur) 40(2):5

    Article  MathSciNet  Google Scholar 

  7. Smeulders AWM et al (2000) Content-based image retrieval at the end of the early years. IEEE Trans Pattern Anal Mach Intell 12:1349–1380

    Article  Google Scholar 

  8. Malviya N, Choudhary N, Jain K (2017) Content based medical image retrieval and clustering based segmentation to diagnose lung cancer. Adv Comput Sci Technol 10(6):1577–1594

    Google Scholar 

  9. Roy S et al (2014) Three-dimensional spatiotemporal features for fast content-based retrieval of focal liver lesions. IEEE Trans Biomed Eng 61(11):2768–2778

    Article  Google Scholar 

  10. Satish B, Supreethi KP (2017) Content based medical image retrieval using relevance feedback Bayesian network. In: 2017 International conference on electrical, electronics, communication, computer, and optimization techniques (ICEECCOT). IEEE

  11. Ghodsi A (2006) Dimensionality reduction a short tutorial. Department of Statistics and Actuarial Science, University of Waterloo, Ontario, Canada 37:38

  12. Pedronette DCG, Torres RS (2016) A correlation graph approach for unsupervised manifold learning in image retrieval tasks. Neurocomputing 208:66–79

    Article  Google Scholar 

  13. Webber W, Moffat A, Zobel J (2010) A similarity measure for indefinite rankings. ACM Trans Inf Syst (TOIS) 28(4):20

    Article  Google Scholar 

  14. Ma L, Liu X, Gao Y, Zhao Y, Zhao X, Zhou C (2017) A new method of content based medical image retrieval and its applications to CT imaging sign retrieval. J Biomed Inf 66:148–158

    Article  Google Scholar 

  15. Wang J, Li J, Han X-H, Lin L, Hu H, Xu Y, Chen Q, Iwamoto Y, Chen Y-W (2019) Tensor-based sparse representations of multi-phase medical images for classification of focal liver lesions. Pattern Recognit Lett. https://doi.org/10.1016/j.patrec.2019.01.001

    Article  Google Scholar 

  16. Pedronette DCG, Gonçalves FMF, Guilherme IR (2018) Unsupervised manifold learning through reciprocal kNN graph and connected components for image retrieval tasks. Pattern Recognit 75:161–174

    Article  Google Scholar 

  17. Conjeti S (2018) Learning to hash for large-scale medical image retrieval. Ph.D. Dissertation, Technische Universität München

  18. Qayyum A et al (2017) Medical image retrieval using deep convolutional neural network. Neurocomputing 266:8–20

    Article  Google Scholar 

  19. Tarjan R (1972) Depth-first search and linear graph algorithms. SIAM J Comput 1(2):146–160

    Article  MathSciNet  Google Scholar 

  20. Xu Y, Lin L, Hu H, Wang D, Liu Y, Wang J, Han X-H, Chen Y-W (2018) Texture-specific bag of visual words model and spatial cone matching based method for the retrieval of focal liver lesions using multiphase contrast-enhanced CT images. Int J Comput Assist Radiol Surg 13(1):151–164

    Article  Google Scholar 

  21. Sokolova M, Lapalme G (2009) A systematic analysis of performance measures for classification tasks. Inf Process Manag 45(4):427–437

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Yen-Wei Chen, Ritsumeikan University, Kansai, for the use of their images in this study.

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Engineering Faculty, Shahed University, Persian Gulf Highway, Tehran, 33191-18651, Iran

    Mansoureh Sadat Mirasadi & Amir Hossein Foruzan

Authors
  1. Mansoureh Sadat Mirasadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amir Hossein Foruzan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amir Hossein Foruzan.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirasadi, M.S., Foruzan, A.H. Content-based medical image retrieval of CT images of liver lesions using manifold learning. Int J Multimed Info Retr 8, 233–240 (2019). https://doi.org/10.1007/s13735-019-00179-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-019-00179-6

Keywords

Search

Navigation