Advertisement

Automatic Heart and Vessel Segmentation Using Random Forests and a Local Phase Guided Level Set Method

  • Chunliang Wang
  • Qian Wang
  • Örjan Smedby
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10129)

Abstract

In this report, a novel automatic heart and vessel segmentation method is proposed. The heart segmentation pipeline consists of three major steps: heart localization using landmark detection, heart isolation using statistical shape model and myocardium segmentation using learning based voxel classification and local phase analysis. In our preliminary test, the proposed method achieved encouraging results.

Keywords

Image segmentation Level set Coherent propagation Local phase analysis Shape model 

References

  1. 1.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imag. 27, 1668–1681 (2008)CrossRefGoogle Scholar
  2. 2.
    Belaid, A., Boukerroui, D., Maingourd, Y., Lerallut, J.-F.: Phase-based level set segmentation of ultrasound images. IEEE Trans. Inf. Technol. Biomed. 15, 138–147 (2011)CrossRefGoogle Scholar
  3. 3.
    Wang, C., Smedby, Ö.: Model-based left ventricle segmentation in 3d ultrasound using phase image. In: presented at the MICCAI Challenge on Echocardiographic Three-Dimensional Ultrasound Segmentation (CETUS), Boston (2014)Google Scholar
  4. 4.
    Wang, C., Smedby, Ö.: Multi-organ segmentation using shape model guided local phase analysis. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 149–156. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24574-4_18 CrossRefGoogle Scholar
  5. 5.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61, 55–79 (2005)CrossRefGoogle Scholar
  6. 6.
    Wang, C., Lundström, C.: CT scan range estimation using multiple body parts detection: let PACS learn the CT image content. Int. J. Comput. Assist. Radiol. Surg. 11, 317–325 (2016)CrossRefGoogle Scholar
  7. 7.
    Knutsson, H., Granlund, G.H.: Signal Processing for Computer Vision. Springer, Heidelberg (1994)Google Scholar
  8. 8.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Technology and Health (STH)KTH Royal Institute of TechnologyStockholmSweden
  2. 2.School of Biomedical Engineering, Med-X Research InstituteShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations