Skip to main content
SpringerLink
Log in

Non-rigid registration of multi-phase liver CT data using fully automated landmark detection and TPS deformation

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In case of the complicated anatomical structure of the liver, landmark points on a three dimensional (3D) liver surface is hardly distinguished as corresponding pairs visually and automated landmark placing will be extremely time saving for liver registration. This paper presents a fully automated landmark detection method to register livers on multi-phase computed tomography (CT) images. Edge texture features and Support Vector Machine (SVM) are applied to detect the discriminated landmarks of the liver, including both surface and internal points. Using the information of liver shape, 3D gray level co-occurrence matrix is calculated into texture features, from which the most informatics there features are selected by our optimization algorithm for choosing a sub-set of features from a high dimensional feature set. Then automated landmarks detection begins at scanning surface points on the pre-contrast and portal venous phase images, where positive outputs of the SVM classifier are regarded as initial candidates and final candidates are obtained by eliminating false positives (FPs). Finally, relied on the detected landmarks, thin plate splines (TPS) algorithm is used to register livers. Five surface landmarks, together with internal landmarks of the liver center from every 25 mm slice interval, can be detected automatically with sensitivity of 88.33% and accuracy of 98.5%. Surface-based mean error (SME) is decreased from 3.80 to 2.87 mm on average, while SME value has increased 32.4 and 8.0% on average respectively when comparing with the rigid and B-spline methods. The results demonstrate that edge textures and SVM classifier are effective in the automated landmark detection. Together with TPS algorithm, fully automated liver registration is able to be achieved on multi-phase CT images.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Sotiras, et al.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)

    Article  Google Scholar 

  2. Oliveira, F.P., Tavares, J.M.: Medical image registration: a review. Comput. Methods Biomech. Biomed. Eng. 17(2), 73–93 (2014)

    Article  Google Scholar 

  3. Nicole, W., Maryam, R., Marta, H., Akram, S.: Utility of dual phase liver CT for metastatic melanoma staging and surveillance. Eur. J. Radiol. 82(12), 2189–2193 (2013)

    Article  Google Scholar 

  4. Deguchi, D., Hayashi, Y., Kitasaka, T., Mori, K., Mekada, Y., Suenaga, Y., Hasegawa, J., Toriwaki, J.: A method for automated liver region extraction basing upon estimation of CT value distributions from multi-phase CT images. J. Comput. Aided Diagn. Med. Images 9(4), 36–48 (2005)

    Google Scholar 

  5. Zhang, X., Lee, G., Tajima, T., Kitagawa, T., Kanematsu, M., Zhou, X., Hara, T., Fujita, H., Yokoyama, R., Kondo, H., Hoshi, H., Nawano, S., Shinozaki, K.: Segmentation of liver region with tumorous tissues. In: Proc. SPIE, CA, p. 6512 (2007)

  6. Diamant, I., Oldberger, J., Klang, E., Amitai, M., Greenspan, H.: Multi-phase liver lesions classification using relevant visual words based on mutual information. In: IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, pp. 407–410 (2015)

  7. Zhang, X., Gao, X., Liu, B.J., Wen, Y., Long, L., Huang, Y., Fujita, H.: Effective staging of fibrosis by the selected texture features of liver: Which one is better, CT or MR imaging? Comput. Med. Imaging Graph. 46, 227–236 (2015)

    Article  Google Scholar 

  8. Quatrehomme, A., Millet, I., Hoa, D., Subsol, G., Puech, W.: Assessing the classification of liver focal lesions by using multi-phase computer tomography scans. In: Proc. of the Third MICCAI International Conference on Medical Content-Based Retrieval for Clinical Decision Support, Nice, vol. 7723, pp. 80–91 (2012)

  9. Foruzan, A.H., Motlagh, H.R.: Multimodality liver registration of open-MR and CT scans. Int. J. CARS. 10, 1–15 (2015)

    Article  Google Scholar 

  10. Penney, G.P., Blackall, J.M., Hamady, M.S., Sabharwal, T., Adam, A., Hawkes, D.J.: Registration of freehand 3d ultrasound and magnetic resonance liver images. Med. Image Anal. 8(1), 81–91 (2004)

    Article  Google Scholar 

  11. Lange, T., Papenberg, N., Heldmann, S., Modersitzki, J., Fischer, B., Lamecker, H., Schlag, P.M.: 3D ultrasound-ct registration of the liver using combined landmark-intensity information. Int. J. CARS. 4(1), 79–88 (2009)

    Article  Google Scholar 

  12. Joseph, V.H., Derek, L.G.H., David, I.H.: Medical Image Registration, pp. 5–6. CRC Press, Boca Raton (2001)

    Google Scholar 

  13. Khallaghi, S., et al.: Statistical biomechanical surface registration: application to MR-TRUS fusion for prostate interventions. IEEE Trans. Med. Imaging 34(12), 2535–2549 (2015)

    Article  Google Scholar 

  14. Carrillo, A., Duerk, J.L., Lewin, J.S., Wilson, D.L.: Semiautomatic 3-D image registration as applied to interventional MRI liver cancer treatment. IEEE Trans. Med. Imaging 19(3), 175–185 (2000)

    Article  Google Scholar 

  15. Weon, C., Hyun, N.W., Lee, D., Lee, J.Y., Ra, J.B.: Position tracking of moving liver lesion based on real-time registration between 2D ultrasound and 3D preoperative images. Med. Phys. 42(1), 335 (2015)

    Article  Google Scholar 

  16. Nemoto, M., Masutani, Y., Hanaoka, S.: A unified framework for concurrent detection of anatomical landmarks for medical image understanding. In: Proc. of SPIE - The International Society for Optical Engineering, Florida, pp. 215–230 (2011)

  17. Pantazis, D., Joshi, A.J., Shattuck, D.W., Bernstein, L.E., Damasio, H., Leahy, R.M.: Comparison of landmark-based and automatic methods for cortical surface registration. Neuroimage 49(3), 2479–2493 (2010)

    Article  Google Scholar 

  18. Erdt, M., Sakas, G., Hammon, M., Beni, S.D., Solbiati, L., Cavallaro, A.: Automatic shape based deformable registration of multiphase contrast enhanced liver CT volumes. In: Proc. of SPIE - The International Society for Optical Engineering, Florida, pp. 765–768 (2011)

  19. Rohr, K., Stiehl, H.S., Sprengel, R., Buzug, T.M., Weese, J., Kuhn, M.H.: Landmark-based elastic registration using approximating thin plate splines. IEEE Trans. Med. Imag. 20(6), 526–534 (2001)

    Article  Google Scholar 

  20. Drechsler, K., Laura, C.O., Chen, Y., Erdt, M.: Semi-automatic anatomical tree matching for landmark-based elastic registration of liver volumes. J. Healthc. Eng. 1(1), 101–123 (2010)

    Article  Google Scholar 

  21. Yi, S., Totz, J., Thompson, S., Johnsen, S., Barratt, D., Schneider, C., Gurusamy, K., Davidson, B., Ourselin, S., Hawkes, D., Clarkson, M.J.: Locally rigid, vessel-based registration for laparoscopic liver surgery. Int. J. CARS. 10(12), 1951–1961 (2015)

    Article  Google Scholar 

  22. Allaire, S., Kim, J.J., Breen, S.L., Jaffray, D.A., Pekar, V.: Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In: IEEE Computer Society Conference on CVPRW, Anchorage, AK, pp. 1–8 (2008)

  23. Paganelli, C., Peroni, M., Riboldi, M., Sharp, G.C., Ciardo, D., et al.: Scale invariant feature transform in adaptive radiation therapy: a tool for deformable image registration assessment and re-planning indication. Phys. Med. Biol. 58(2), 287–299 (2013)

    Article  Google Scholar 

  24. Toews, M.: WW Rd efficient and robust model-to-image alignment using 3D scale-invariant features. Med. Image Anal. 17(3), 271–282 (2013)

    Article  Google Scholar 

  25. Paganelli, C., Summers, P., Gianoli, C., Bellomi, M., Baroni, G., et al.: A tool for validating MRI guided strategies: a digital breathing CT/MRI phantom of the abdominal site. Med. Biol. Eng. Comput. 6, 1–14 (2017)

    Google Scholar 

  26. Ding, S., Miga, M.I., Noble, J.H., Dumpuri, P., Cao, A., Thompson, R.C.: Semiautomatic registration of pre- and postbrain tumor resection laser range data: method and validation. IEEE Trans. Biomed. Eng. 56(3), 770–780 (2009)

    Article  Google Scholar 

  27. Smistad, E., Lindseth, F.: Real-time automatic artery segmentation, reconstruction and registration for ultrasound-guided regional anaesthesia of the femoral nerve. IEEE Trans. Med. Imaging 35(3), 752–761 (2016)

    Article  Google Scholar 

  28. Bentoutou, Y., Taleb, N.: Automatic extraction of control points for digital subtraction angiography image enhancement. IEEE Trans. Nucl. Sci. 52(1), 238–246 (2005)

    Article  Google Scholar 

  29. Liu, J., Gao, W., Huang, S., Nowinski, W.L.: A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages. IEEE Trans. Med. Imaging 27(8), 1034–1044 (2008)

    Article  Google Scholar 

  30. Mitra, J., Oliver, A., Marti, R., Llado, X., Vilanova, J.C., Meriaudeau, F.: A thin-plate spline based multimodal prostate registration with optimal correspondences. In: Sixth International Conference on Signal - Image Technology and Internet-Based Systems (SITIS), Kuala Lumpur, pp. 7–11 (2010)

  31. Rohr, K.: Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties. In: Proc. of the 1st International Conference on Medical Image Computing and Computer-Assisted Intervention. MA, MICCAI’98, vol. 1496, pp. 1174–1183 (1998)

  32. Lee, J., Kim, K.W., Kim, S.Y., Shin, J., Park, K.J., Won, H.J., Shin, Y.M.: Automatic detection method of hepatocellular carcinomas using the non-rigid registration method of multi-phase liver CT images. J. X-Ray Sci. Technol. 23(3), 275–288 (2015)

    Article  Google Scholar 

  33. Tang, S., Wang, Y.: MR-guided liver cancer surgery by non rigid registration. In: International Conference on Medical Image Analysis and Clinical Applications (MIACA), Guangdong, pp. 113–117 (2010)

  34. Bookstein, F.L.: Principal warps: thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11(6), 567–585 (1989)

    Article  Google Scholar 

  35. Evans, A.C., Dai, W., Collins, D.L., Neelin, P., Marrett, S.: Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis. In: Proc. of SPIE - The International Society for Optical Engineering, San Jose, pp. 236–246 (1992)

  36. Xiaoyang, H., Boliang, W., Ruhuan, L., Xiaoping, W., Zhijian, W.: CT-MR image registration in liver treatment by maximization of mutual information. In: IEEE International Symposium on IT in Medicine and Education (ITME), Xiamen, pp. 715–718 (2008)

  37. Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 16(2), 187–198 (1997)

    Article  Google Scholar 

  38. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973)

    Article  Google Scholar 

  39. Siqueira, F.R.D., Schwartz, W.R., Pedrini, H.: Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120(10), 336–345 (2013)

    Article  Google Scholar 

  40. Rivas, E.C., Moreno, F., Benitez, A., Morocho, V., Vanegas, P., Medina, R.: Hepatic Steatosis detection using the co-occurrence matrix in tomography and ultrasound images. In: 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), Bogota, pp. 1–7 (2015)

  41. Zeng, Y.F., Zhang, X.J., Yan, W., Long, L.L., Huang, Y.K., Shi, J.X., Liang, T., Huang, Y.H.: Computer aided interpretation of fibrous texture in hepatic magnetic resonance images. Adv. Mater. Res. 647, 325–330 (2001)

    Article  Google Scholar 

  42. Zhang, X., Gao, X., Liu, B.J., Ma, K., Yan, W., Long, L.L., Yuhong, H., Fujita, H.: Effective staging of fibrosis by the selected texture features of liver: which one is better, CT or MR imaging? Comput. Med. Imag. Grap. 46, 227–236 (2015)

    Article  Google Scholar 

  43. Zhang, X., Zhou, B., Ma, K., Qu, X., Tan, X., Gao, X., Yan, W., Long, L.L., Fujita, H.: Selection of optimal shape features for staging hepatic fibrosis on CT image. J. Med. Imag. Health Inform. 5(8), 1926–1930 (2015)

    Article  Google Scholar 

  44. Lin, K.P., Chen, M.S.: On the design and analysis of the privacy-preserving SVM classifier. IEEE Trans. Knowl. Data Eng. 23(11), 1704–1717 (2011)

    Article  Google Scholar 

  45. Xu, Z., Lee, C.P., Herinrich, M.P., Modat, M., Rueckert, D., Ourselin, S., Abramson, R.G., Landman, B.A.: Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans. Biomed. Eng. 63(8), 1563–1572 (2016)

    Article  Google Scholar 

  46. Heinrich, M.P., Jenkinson, M., Brady, S.M., Schnabel, J.A.: Textural mutual information based on cluster trees for multimodal deformable registration. In: 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, pp. 1471–1474 (2012).

  47. Zhou, X., Takayama, R., Wang, S., Hara, T., Fujita, H.: Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med. Phys. 44(10), 5221–5233 (2017)

    Article  Google Scholar 

  48. Johnson, H.J., McCormick, M.M., Ibanez, L.: The ITK Software Guide Book 1: Introduction and Development Guidelines, 4th edn. Kitware Inc, Clifton Park (2016)

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 81460274), the National Natural Science Foundation of China (Grant: 81760324). This work was supported in part by JSPS Grant-in-Aid for Scientific Research on Innovative Areas (Grant Number 26108005). Funding was provided by Health and Family planning Commission of Guangxi (Grant Number Z2016762).

Author information

Authors and Affiliations

  1. School of Computer, Electronics and Information, Guangxi University, Nanning, People’s Republic of China

    Xuejun Zhang & Xiaomin Tan

  2. Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, People’s Republic of China

    Xin Gao

  3. Guangxi Key Laboratory of Multimedia Communications and Network Technology, Guangxi University, Nanning, People’s Republic of China

    Xuejun Zhang

  4. People’s Hospital of Guangxi Zhuang Nationality Autonomous Region, Nanning, China

    Dongbo Wu

  5. Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, Gifu, 501-1154, Japan

    Xiangrong Zhou & Hiroshi Fujita

Authors
  1. Xuejun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaomin Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xin Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dongbo Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiangrong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hiroshi Fujita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin Gao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, X., Tan, X., Gao, X. et al. Non-rigid registration of multi-phase liver CT data using fully automated landmark detection and TPS deformation. Cluster Comput 22 (Suppl 6), 15305–15319 (2019). https://doi.org/10.1007/s10586-018-2567-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2567-3

Keywords

Search

Navigation