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Intensity Inhomogeneity Quantization-Based Variational Model for Segmentation of Hepatocellular Carcinoma (HCC) in Computed Tomography (CT) Images

  • Luying Gui
  • Xiaoping Yang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 506)

Abstract

In this paper, we propose a novel quantity to measure the complexity of regions with inhomogeneous intensity in images. In order to describe real boundaries of objects, we further design an edge detector which is based on the similarity between object regions and those around them. Imbedding these two measurements of inhomogeneous regions into a level set framework, the proposed model is applied to segment HCC regions in CT images with promising results. Additionally, benefitting from the two measurements, segmentation is robust with respect to the initialization. Comparison results also confirm that the proposed method is more accurate than two well-known methods, the CV model and the BCS model, on segmenting objects with inhomogeneities.

Notes

Acknowledgements

This work is supported by National Nature Science Foundation of China (No. 91330101 and NO.11531005).

References

  1. 1.
    Yu J, Wang Y, Chen P (2008) Fetal ultrasound image segmentation system and its use in fetal weight estimation. Med Biol Eng Comput 46(12):1227–1237CrossRefGoogle Scholar
  2. 2.
    Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation 1. Ann Rev Biomed Eng 2(1):315–337CrossRefGoogle Scholar
  3. 3.
    Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25(8):987–1010CrossRefGoogle Scholar
  4. 4.
    Gutiérrez-Becker B, Cosío FA, Huerta MEG, Benavides-Serralde JA, Camargo-Marín L, Bañuelos VM (2013) Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3d statistical shape model. Med Biol Eng Comput 51(9):1021–1030CrossRefGoogle Scholar
  5. 5.
    Zhang D, Liu Y, Yang Y, Xu M, Yan Y, Qin Q (2016) A region-based segmentation method for ultrasound images in hifu therapy. Med Phys 43(6):2975–2989CrossRefGoogle Scholar
  6. 6.
    Roy S, Nag S, Maitra IK, Bandyopadhyay SK (2013) A review on automated brain tumor detection and segmentation from MRI of brain. arXiv preprint arXiv:1312.6150Google Scholar
  7. 7.
    Huang J, Yang X, Chen Y, Tang L (2013) Ultrasound kidney segmentation with a global prior shape. J Vis Commun Image Represent 24(7):937–943CrossRefGoogle Scholar
  8. 8.
    Qiu W, Yuan J, Ukwatta E, Sun Y, Rajchl M, Fenster A (2014) Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3-d trus and mr images. IEEE Trans Med Imaging 33(4):947–960CrossRefGoogle Scholar
  9. 9.
    Gloger O, Tönnies KD, Liebscher V, Kugelmann B, Laqua R, Völzke H (2012) Prior shape level set segmentation on multistep generated probability maps of mr datasets for fully automatic kidney parenchyma volumetry. IEEE Trans Med Imaging 31(2):312–325CrossRefGoogle Scholar
  10. 10.
    Yang F, Qin W, Xie Y, Wen T, Gu J (2012) A shape-optimized framework for kidney segmentation in ultrasound images using nltv denoising and drlse. Biomed Eng Online 11(1):82CrossRefGoogle Scholar
  11. 11.
    Gui L, He J, Qiu Y, Yang X (2017) Integrating compact constraint and distance regularization with level set for hepatocellular carcinoma (hcc) segmentation on computed tomography (ct) images. Sens Imaging 18(1):4CrossRefGoogle Scholar
  12. 12.
    Xie J, Jiang Y, Tsui HT (2005) Segmentation of kidney from ultrasound images based on texture and shape priors. IEEE Trans Med Imaging 24(1):45–57CrossRefGoogle Scholar
  13. 13.
    Wu CH, Sun YN (2006) Segmentation of kidney from ultrasound b-mode images with texture-based classification. Comput Methods Prog Biomed 84(2):114–123CrossRefGoogle Scholar
  14. 14.
    Liu B, Cheng H, Huang J, Tian J, Tang X, Liu J (2010) Probability density difference-based active contour for ultrasound image segmentation. Pattern Recogn 43(6):2028–2042zbMATHCrossRefGoogle Scholar
  15. 15.
    He L, Zheng S, Wang L (2010) Integrating local distribution information with level set for boundary extraction. J Vis Commun Image Represent 21(4):343–354CrossRefGoogle Scholar
  16. 16.
    Vivekanantham S, Azzopardi G, Prashanth Ravindran G (2014) Active contours driven by the salient edge energy model. Br J Hosp Med (Lond: 2005) 75(4):236Google Scholar
  17. 17.
    Hou Z (2006) A review on mr image intensity inhomogeneity correction. Int J Biomed Imaging 2006Google Scholar
  18. 18.
    Li C, Gore JC, Davatzikos C (2014) Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. Magn Reson Imaging 32(7):913–923CrossRefGoogle Scholar
  19. 19.
    Li C, Huang R, Ding Z, Gatenby J, Metaxas DN, Gore JC (2011) A level set method for image segmentation in the presence of intensity inhomogeneities with application to mri. IEEE Trans Image Process 20(7):2007–2016MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Wang L, Chen Y, Pan X, Hong X, Xia D (2010) Level set segmentation of brain magnetic resonance images based on local Gaussian distribution fitting energy. J Neurosci Methods 188(2):316–325CrossRefGoogle Scholar
  21. 21.
    Wang L, Pan C (2014) Image-guided regularization level set evolution for MR image segmentation and bias field correction. Magn Reson Imaging 32(1):71–83CrossRefGoogle Scholar
  22. 22.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277zbMATHCrossRefGoogle Scholar
  23. 23.
    Powers DM (2011) Evaluation: from precision, recall and f-measure to roc, informedness, markedness & correlation. J Mach Learn Technol 2(1):37–63MathSciNetGoogle Scholar
  24. 24.
    Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Luying Gui
    • 1
  • Xiaoping Yang
    • 2
  1. 1.Nanjing University of Science and TechnologyNanjingChina
  2. 2.Nanjing UniversityNanjingChina

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