Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images

  • Published:
Journal of Digital Imaging Aims and scope Submit manuscript

Abstract

This paper is aimed at developing and evaluating a content-based retrieval method for contrast-enhanced liver computed tomographic (CT) images using bag-of-visual-words (BoW) representations of single and multiple phases. The BoW histograms are extracted using the raw intensity as local patch descriptor for each enhance phase by densely sampling the image patches within the liver lesion regions. The distance metric learning algorithms are employed to obtain the semantic similarity on the Hellinger kernel feature map of the BoW histograms. The different visual vocabularies for BoW and learned distance metrics are evaluated in a contrast-enhanced CT image dataset comprised of 189 patients with three types of focal liver lesions, including 87 hepatomas, 62 cysts, and 60 hemangiomas. For each single enhance phase, the mean of average precision (mAP) of BoW representations for retrieval can reach above 90 % which is significantly higher than that of intensity histogram and Gabor filters. Furthermore, the combined BoW representations of the three enhance phases can improve mAP to 94.5 %. These preliminary results demonstrate that the BoW representation is effective and feasible for retrieval of liver lesions in contrast-enhanced CT images.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Müller H, Michoux N, Bandon D, Geissbuhler A: A review of content-based image retrieval systems in medical applications–clinical benefits and future directions. Int J Med Informatics 73(1):1–23, 2004

    Article  Google Scholar 

  2. Chen EL, Chung PC, Chen CL, Tsai HM, Chang CI: An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng 45(6):783–794, 1998

    Article  PubMed  CAS  Google Scholar 

  3. Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS, Kelekis D: A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Trans Info Tech Biomed 7(3):153–162, 2003

    Article  Google Scholar 

  4. Bilello M, Gokturk SB, Desser T, Napel S, Jeffrey Jr, RB, Beaulieu CF: Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT. Med Phys 31(9):2584–2593, 2004

    Article  PubMed  Google Scholar 

  5. Mougiakakou SG, Valavanis IK, Nikita A, Nikita KS: Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif Intell Med 41(1):25–37, 2007

    Article  PubMed  Google Scholar 

  6. Napel SA, et al: Automated retrieval of CT images of liver lesions on the basis of image similarity: method and preliminary results. Radiology 256(1):243–252, 2010

    Article  PubMed  Google Scholar 

  7. Ye J, Sun Y, Wang S, Gu L, Qian L, Xu J: Multi-phase CT image based hepatic lesion diagnosis by SVM. In the 2nd International Conference on Biomedical Engineering and Informatics 2009

  8. Nino-Murcia M, Olcott EW, Jeffrey RB, Lamm RL, Beaulieu CF, Jain KA: Focal liver lesions: pattern-based classification scheme for enhancement at arterial phase CT. Radiology 215(3):746–751, 2000

    PubMed  CAS  Google Scholar 

  9. Akgül C, Rubin D, Napel S, Beaulieu C, Greenspan H, Acar B: Content-based image retrieval in radiology: current status and future directions. J Digit Imaging 24(2):202–222, 2011

    Article  Google Scholar 

  10. Zhao CG, Cheng HY, Huo YL, Zhuang TG: Liver CT-image retrieval based on Gabor texture. In EMBS, 2004

  11. Lee C-C, Chen S-H, Tsai H-M, Chung P-C, Chiang Y-C: Discrimination of liver diseases from CT images based on Gabor filters. In the 19th IEEE Symposium on Computer-Based Medical Systems, 2006

  12. El-Gendy MM, El-Zahraa Bou-Chadi F: An automated system for classifying computed tomographic liver images. In National Radio Science Conference, 2009

  13. Varma M, Zisserman A: A statistical approach to texture classification from single images. Int J Comput Vis 62(1):61–81, 2005

    Google Scholar 

  14. Manik V, Andrew Z: A statistical approach to material classification using image patch exemplars. IEEE Trans Pattern Anal Mach Intell 31(11):2032–2047, 2009

    Article  Google Scholar 

  15. Li F-F, Perona P: A bayesian hierarchical model for learning natural scene categories. In IEEE Conference on Computer Vision and Pattern Recognition, 2005

  16. Winn J, Criminisi A, Minka T: Object categorization by learned universal visual dictionary. In International Conference on Computer Vision, 2005

  17. Florent P: Universal and adapted vocabularies for generic visual categorization. IEEE Trans Pattern Anal Mach Intell 30(7):1243–1256, 2008

    Article  Google Scholar 

  18. van Gemert JC, Snoek CGM, Veenman CJ, Smeulders AWM, Geusebroek J-M: Comparing compact codebooks for visual categorization. Computer Vision and Image Understanding 114(4):450–462, 2010

    Article  Google Scholar 

  19. Jégou H, Douze M, Schmid C: Improving bag-of-features for large scale image search. Int J Comput Vis 87(3):316–336, 2011

    Article  Google Scholar 

  20. Avni U, Greenspan H, Sharon M, Konen E, Goldberger J: X-ray image categorization and retrieval using patch-based visual words representation. In the Sixth IEEE International Conference on Symposium on Biomedical Imaging, 2009

  21. Deserno T, Antani S, Long R: Ontology of gaps in content-based image retrieval. J Digit Imaging 22(2):202–215, 2009

    Article  PubMed  Google Scholar 

  22. van Gemert JC, Veenman CJ, Smeulders AWM, Geusebroek J-M: Visual Word Ambiguity. IEEE Trans Pattern Anal Mach Intell 32(7):1271–1283, 2009

    Article  Google Scholar 

  23. Lowe DG: Distinctive image features from scale-Invariant keypoints. Int J Comput Vis 60(2):91–110, 2004

    Article  Google Scholar 

  24. Coates A, Lee H, Ng AY: An analysis of single-layer networks in unsupervised feature learning. In the 14th International Conference on Artificial Intelligence and Statistics (AISTATS), 2011

  25. Wojcikiewicz W, Binder A, Kawanabe M: Enhancing image classification with class-wise clustered vocabularies. In the 20th International Conference on Pattern Recognition, 2010

  26. Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y: Locality-constrained Linear Coding for Image Classification. In IEEE Conference on Computer Vision and Pattern Recognition, 2010

  27. Chang H, Yeung D-Y: Kernel-based distance metric learning for content-based image retrieval. Image and Vision Computing 25(5):695–703, 2007

    Article  Google Scholar 

  28. Masashi S: Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. J Mach Learn Res 8:1027–1061, 2007

    Google Scholar 

  29. Xing EP, Ng AY, Jordan MI, Russell SJ: Distance metric learning with application to clustering with side-information. In Conference on Neural Information Processing Systems (NIPS), 2002

  30. Weinberger KQ, Saul LK: Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10:207–244, 2009

    Google Scholar 

  31. Alipanahi B, Biggs M, Ghodsi A: Distance metric learning vs. Fisher discriminant analysis. In the 23rd national conference on Artificial intelligence, 2008

  32. Zhang Z, Dai G, Xu C, Jordan MI: Regularized discriminant analysis, ridge regression and beyond. J Mach Learn Res 11(3):2199–2228, 2010

    Google Scholar 

  33. Perronnin F, Senchez J, Xerox Y: Large-scale image categorization with explicit data embedding. In IEEE Conference on Computer Vision and Pattern Recognition, 2010

  34. Vedaldi A, Zisserman A: Efficient additive kernels via explicit feature maps. In IEEE Conference on Computer Vision and Pattern Recognition, 2010

  35. Manjunath BS, Ma WY: Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18(8):837–842, 1996

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by grants from the National Basic Research Program of China (973 Program) (no. 2010CB732505) and National Natural Science Funds of China (no. 81101109, no. 30900380 and no. 31000450).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qianjin Feng.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, W., Lu, Z., Yu, M. et al. Content-Based Retrieval of Focal Liver Lesions Using Bag-of-Visual-Words Representations of Single- and Multiphase Contrast-Enhanced CT Images. J Digit Imaging 25, 708–719 (2012). https://doi.org/10.1007/s10278-012-9495-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10278-012-9495-1

Keywords