Skip to main content
SpringerLink
Log in

3D reconstruction for ultrasonic C-scan images of tissue-mimicking phantom based on an improved K-nearest neighbor filtering

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Although the ultrasonic C-scan technique has been extensively applied in nondestructive testing (NDT) in recent years, 3D reconstruction from ultrasonic C-scan images has not been well addressed. This paper develops a novel and efficient 3D reconstruction technique based on an improved K-nearest neighbor filtering for ultrasonic C-scan data of the tissue-mimicking phantoms. An edge-points-predicting approach based on K-nearest neighbor filtering is first proposed to predict the undetected edge points and to reduce the noise points for 2D ultrasonic images. Then, the 3D model is reconstructed from the clean edges by utilizing the surface rendering algorithm. The proposed approach is validated using the ultrasonic C-scan data of a liver model embedded in a tissue-mimicking phantom. The comparisons with other methods are presented in the experiments. The results demonstrate the effectiveness and the significantly improved reconstruction results of the proposed approach.

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
Fig. 14

Similar content being viewed by others

References

  1. Angelopoulou A, Psarrou A, Garcia-Rodriguez J, Orts-Escolano S, Azorin-Lopez J (2015) 3D reconstruction of medical images from slices automatically landmarked with growing neural models. Neurocomputing 150(PA):16–25

    Article  Google Scholar 

  2. Barry CD, Gee AH, Berman L (1997) Three-dimensional freehand ultrasound: image reconstruction and volume analysis. Ultrasound Med Biol 23(8):1209–1224

    Article  Google Scholar 

  3. Baselice F (2017) Ultrasound image despeckling based on statistical similarity. Ultrasound Med Biol 43(9):2065–2078

    Article  Google Scholar 

  4. Cai S, Huang J, Chen J, Huang Y, Ding X, Zeng D (2018) Prominent edge detection with deep metric expression and multi-scale features. Multimed Tools Appl. https://doi.org/10.1007/s11042-018-6581-5

  5. Chen Y, Navarro L, Wang Y, Courbebaisse G (2014) Segmentation of the thrombus of giant intracranial aneurysms from CT angiography scans with lattice Boltzmann method. Med Image Anal 18(1):1–8

    Article  Google Scholar 

  6. Chikmurge D, Harnale S (2018) Feature extraction of DICOM images using canny edge detection algorithm. Adv Intell Syst Comput 632:185–196

    Google Scholar 

  7. Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27

    Article  MATH  Google Scholar 

  8. Ferrante E, Paragios N (2017) Slice-to-volume medical image registration: a survey. Med Image Anal 39:101–123

    Article  Google Scholar 

  9. Ghose S, Dowling JA, Rai R, Liney GP (2017) Substitute CT generation from a single ultra short time echo MRI sequence: preliminary study. Phys Med Biol 62(8):2950–2960

    Article  Google Scholar 

  10. Huang Q, Zeng Z (2017) A review on real-time 3D ultrasound imaging technology. Biomed Res Int 2017:6027029. https://doi.org/10.1155/2017/6027029

    Article  Google Scholar 

  11. Huang QH, Zheng YP (2009) A new adaptive interpolation algorithm for 3D ultrasound imaging with speckle reduction and edge preservation. Comput Med Imaging Graph 33(2):100–110

    Article  MathSciNet  Google Scholar 

  12. Jaffar MA, Zia S, Latif G, Mirza AM, Mehmood I et al (2012) Anisotropic diffusion based brain MRI segmentation and 3D reconstruction. Int J Comput Int Sys 5(3):494–504

    Article  Google Scholar 

  13. Kainz B, Steinberger M, Wein W, Kuklisova-Murgasova M et al (2015) Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans Med Imaging 34(9):1901–1913

    Article  Google Scholar 

  14. karaman M, Wygant IO, Oralkan O, Khuri-Yakub B (2009) Minimally redundant 2-D array designs for 3-D medical ultrasound imaging. IEEE Trans Med Imaging 28(7):1051–1061

    Article  Google Scholar 

  15. Katunin A (2015) Stone impact damage identification in composite plates using modal data and quincunx wavelet analysis. Arch Civ Mech Eng 15:251–261

    Article  Google Scholar 

  16. Katunin A, Dragan K, Dziendzikowski M (2015) Damage identification in aircraft composite structures: a case study using various non-destructive testing techniques. Compos Struct 127:1–9

    Article  Google Scholar 

  17. Kerr W, Rowe P, Pierce SG (2017) Accurate 3D reconstruction of bony surfaces using ultrasonic synthetic aperture techniques for robotic knee arthroplasty. Comput Med Imaging Graph 58:23–32

    Article  Google Scholar 

  18. Khvostikov A, Krylov A, Kamalov J, Megroyan A (2015) Influence of ultrasound despeckling on the liver fibrosis classification. In: 5th International Conference on Image Processing, Theory, Tools and Applications 2015, pp 440–445

  19. Kim K, Habas PA, Rousseau F, Glenn OA et al (2010) Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation. IEEE Trans Med Imaging 29(1):146–158

    Article  Google Scholar 

  20. Kim D, Ramani S, Fessler JA (2015) Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction. IEEE Trans Med Imaging 34(1):167–178

    Article  Google Scholar 

  21. Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA (2012) Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal 16(8):1550–1564

    Article  Google Scholar 

  22. Kumar A, Rajkumar KV, Palanichamy P, Jayakumar T, Chellapandian R, Kasiviswanathan KV, Lande BK (2007) Development and applications of C-scan ultrasonic facility. BARC Newsletter 31(42):14989–14997

    Google Scholar 

  23. Kuo J, Mamou J, Aristizábal O, Zhao X, Ketterling JA, Wang Y (2016) Nested graph cut for automatic segmentation of high-frequency ultrasound images of the mouse embryo. IEEE Trans Med Imaging 35(2):427–441

    Article  Google Scholar 

  24. Mederos, Amenta N, Velho L, Figueiredo LH (2005) Surface reconstruction from noisy point clouds. Eurographics Symposium on Geometry Processing 255:53–62

    Google Scholar 

  25. Milletari F, Navab N, Ahmadi S (2016) V-Net: Fully convolutional neural networks for volumetric medical image segmentation. 4th International Conference on 3D Vision, pp 565–571

  26. Moon H, Ju G, Park S, Shin H (2016) 3D freehand ultrasound reconstruction using a piecewise smooth Markov random field. Comput Vis Image Underst 151:101–113

    Article  Google Scholar 

  27. Poudel P, Illanes A, Arens C, Hansen C, Friebe M (2017) Active contours extension and similarity indicators for improved 3D segmentation of thyroid ultrasound images[C]// Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications. International Society for Optics and Photonicss. SPIE Medical Imaging, Orlando

  28. Quan EM, Lalush DS (2010) Three-dimensional imaging properties of rotation-free square and hexagonal micro-CT systems. IEEE Trans Med Imaging 29(3):916–923

    Article  Google Scholar 

  29. Rim Y, McPherson D, Kim H (2013) Volumetric three dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation. Biomed Eng Online 12(1):39

    Article  Google Scholar 

  30. Rodtook A, Kirimasthong K, Lohitvisate W, Makhanov SS (2018) Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities. Pattern Recogn 79:172–182

    Article  Google Scholar 

  31. Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional networks for biomedical image segmentation. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, vol 9351, pp 234–241

  32. Solberg OV, Lindseth F, Torp H, Blake RE et al (2007) Freehand 3d ultrasound reconstruction algorithms-a review. Ultrasound Med Biol 33(7):991–1009

    Article  Google Scholar 

  33. Toonkum P, Suwanwela NC, Chinrungrueng C (2011) Reconstruction of 3D ultrasound images based on cyclic regularized Savitzky–Golay filters. Ultrasonics 51(2):136–147

    Article  Google Scholar 

  34. Verma J, Nath M, Tripathi P, Saini KK (2017) Analysis and identification of kidney stone using K-th nearest neighbour (KNN) and support vector machine (SVM) classification techniques. Pattern Recognit Image Anal 27(3):574–580

    Article  Google Scholar 

  35. Wang J, Ye M, Liu Z, Wang C (2009) Precision of cortical bone reconstruction based on 3D CT scans. Comput Med Imaging Graph 33(3):235–241

    Article  Google Scholar 

  36. Wen T, Yang F, Gu J, Wang L (2015) A novel Bayesian-based nonlocal reconstruction method for freehand 3D ultrasound imaging. Neurocomputing 168:104–118

    Article  Google Scholar 

  37. Wronkowicz A, Katunin A, Dragan K (2015) Ultrasonic C-scan image processing using multilevel thresholding for damage evaluation in aircraft vertical stabilizer. I J Image, Graphics and Signal Processing11:1–8

  38. Xu Q, Yu H, Mou X, Zhang L, Hsieh J et al (2012) Low-dose X-ray CT reconstruction via dictionary learning. IEEE Trans Med Imaging 31(9):1682–1697

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under grant No.61672084 and the Fundamental Research Funds for the Central Universities under grant No.XK1802-4.

Author information

Authors and Affiliations

  1. College of Information Science &Technology, Beijing University of Chemical Technology, CO, Beijing, 100029, China

    Haijiang Zhu & Tengfei Yang

  2. Acoustic Laboratory, National Institute of Metrology, CO, Beijing, 100029, China

    Ping Yang & Longbiao He

  3. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Guanghui Wang

Authors
  1. Haijiang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tengfei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ping Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Longbiao He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guanghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Haijiang Zhu or Guanghui Wang.

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

Zhu, H., Yang, T., Yang, P. et al. 3D reconstruction for ultrasonic C-scan images of tissue-mimicking phantom based on an improved K-nearest neighbor filtering. Multimed Tools Appl 78, 23597–23616 (2019). https://doi.org/10.1007/s11042-019-7686-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7686-1

Keywords

Search

Navigation