Skip to main content

Advertisement

SpringerLink
Log in

Preserving the border and curvature of fetal heart chambers through TDyWT perspective geometry wrap segmentation

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

Abstract

Congenital heart defect leads to the structural abnormality in neonatal. Congenital heart defect (CHD) is the major cause for 20% perinatal mortality and 50% infant mortality. However, the fetal cardiac screening plays a vital role to diagnose the CHD during second –trimester. Evaluation of fetal cardiac chamber structure is difficult due to small in size and movement. Four-chamber view has become the first and foremost view of echocardiography to detect structural malformations in the fetal heart. Furthermore, trained obstetricians, Pediatric cardiologists, maternal-fetal medicine specialists, and radiologists need proper knowledge and skills to identify the chamber structure from echocardiography. In addition, the effective diagnosis of fetal heart four-chamber view consumes more time and needs extremely skilled radiologists owing to their low quality, small signal to noise ratio and rapid movement of fetal heart ultrasound images. Thus, the diagnosis of CHD is the most challenging task. In this paper, we propose the Transverse Dyadic Wavelet Transform (TDyWT) algorithm to preserve the border and curvature of four chambers from 18 to 22 weeks ultrasound fetal heart image. We validate the proposed TDyWT algorithm with normal and abnormal images from Mediscan radiological centre. The performance of the TDyWT algorithm analyses qualitatively and quantitatively to prove border and curvature of the chambers is superior to other conventional methods.

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.

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

Similar content being viewed by others

References

  1. Allan G et al (2017) Simultaneous analysis of 2D echo views for left atrial segmentation and disease detection. IEEE Trans Med Imaging 36(1):40–50

    Article  MathSciNet  Google Scholar 

  2. Balaji GN, Subashini TS, Chidambaram N (2015) Detection of heart muscle damage from automated analysis of echocardiogram video. IETE J Res 61(3):236–243

    Article  Google Scholar 

  3. Cao Y et al (2014) Segmentation of anatomical structures in four-chamber view echocardiogram images. 22nd International Conference on Pattern Recognition, IEEE https://doi.org/10.1109/ICPR.2014.108

  4. Carneiro G et al (2011) The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process. https://doi.org/10.1109/TIP.2011.2169273

  5. Deng Y, Wang Y, Shen Y, Chen P (2012) Active cardiac model and its application on structure detection from early fetal ultrasound sequences. Comput Med Imaging Graph 36:239–247

    Article  Google Scholar 

  6. Dietenbeck T et al (2012) Detection of the whole myocardium in 2D-echocardiography for multiple orientations using a geometrically constrained level-set. Med Image Anal 16:386–340

    Article  Google Scholar 

  7. Dindoyal I, Lambrou T, Deng J, Todd-Pokropek A (2007) Level set snake algorithms on the fetal heart. In: Proc: 4th IEEE International Symposium on Biomedical Imaging, pp 864–867, April 2007

  8. Dindoyal et al (2011) 2D-3D fetal cardiac dataset segmentation using deformable model. Med Phys 38(7):4338–4349

    Article  Google Scholar 

  9. Guo Y, Wang Y, Nie S, Yu J, Chen P (2014) Automatic segmentation of a fetal echocardiogram using modified active appearance models and sparse representation. IEEE Trans Biomed Eng 61(4):1121–1133

    Article  Google Scholar 

  10. Huang X, Lin BA, Compas CB, Sinusas AJ, Staib LH, Duncan JS (2012) Segmentation of left ventricles from echocardiographic sequences via sparse appearance representation. In: Proc. IEEE Math. Methods Biomed. Image Analy, pp 305–312, Jun. 2012

  11. Huang X et al (2014) Contour tracking in echocardiographic sequences via sparse representation and dictionary learning. Med Image Anal 18:253–271

    Article  Google Scholar 

  12. Leung KYE, Bosch JG (2010) Automated border detection in three-dimensional echocardiography: principles and promises. Eur J Echocardiogr 11:97–108

    Article  Google Scholar 

  13. Lorena et al (2016) Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered Hermite transform. Comput Methods Prog Biomed 137:231–245

    Article  Google Scholar 

  14. Nirmala S, Sridevi S (2016) Markov random field segmentation based sonographic identification of prenatal ventricular septal defect. 7th International Conference on Communication, Computing and Virtualization, Procedia Computer Science vol. 79, pp 344–350

  15. Pedrosa J et al (2016) Left Ventricular myocardial segmentation in 3D ultrasound recordings: effect of different endo and epicardial coupling strategies. IEEE Trans Ultrason Ferroelectr Freq Control 14(8):525–536

    Google Scholar 

  16. Sampath S, Sivaraj N (2014) Fuzzy connectedness based segmentation of fetal heart from clinical ultrasound images. In: Advanced Computing, Networking and Informatics, vol. 1. Springer, Cham, pp 329–337

  17. Sardsud C et al (2015) Patch-based fetal heart chamber segmentation in ultrasound sequences using possibilistic clustering. Seventh International Conference on Computational Intelligence, Modelling and Simulation, IEEE Computer Society

  18. Sridevi S, Nirmala S (2015) ANFIS based decision support system for prenatal detection of Truncus Arteriosus congenital heart defect. Appl Soft Comput J. https://doi.org/10.1016/j.asoc.2015.09.002

  19. Tutschek B, Sahn DJ (2008) Semi-automatic segmentation of fetal cardiac cavities: progress towards an automated fetal echocardiogram. Ultrasound Obstet Gynecol 32:176–180

    Article  Google Scholar 

  20. YEO L et al (2011) Four-chamber view and ‘swing technique’ (FAST) echo: a novel and simple algorithm to visualize standard fetal echocardiographic planes. Ultrasound Obstet Gynecol 37:423–431

    Article  Google Scholar 

  21. Yu L et al (2016) Segmentation of fetal left ventricle in echocardiographic sequences based on dynamic convolutional neural networks. IEEE Trans Biomed Eng. https://doi.org/10.1109/TBME.2016.2628401

Download references

Acknowledgements

The Authors would like to thank the Mediscan systems, Centre for ultrasound, fetal care and genetics, Chennai for providing data and valuable suggestions to carry out this work.

Author information

Authors and Affiliations

  1. R.M.D Engineering College, Kavaraipettai, Chennai, India

    C. Shobana Nageswari & K. Helen Prabha

Authors
  1. C. Shobana Nageswari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Helen Prabha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Shobana Nageswari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nageswari, C.S., Prabha, K.H. Preserving the border and curvature of fetal heart chambers through TDyWT perspective geometry wrap segmentation. Multimed Tools Appl 77, 10235–10250 (2018). https://doi.org/10.1007/s11042-017-5428-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5428-9

Keywords

Search

Navigation