922
Views
4
CrossRef citations to date
0
Altmetric
Research Article

Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT

, , , , &

References

  • Heimann T, Van Ginneken B, Styner MA, et al. Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Trans Med Imaging. 2009;28:1251–1265.
  • Ruskó L, Bekes G, Fidrich M. Automatic segmentation of the liver from multi-and single-phase contrast-enhanced ct images. Med Image Anal. 2009;13:871–882.
  • Jiang Y, Chung FL, Wang S, et al. Collaborative fuzzy clustering from multiple weighted views. IEEE Trans Cybern. 2015;45:688–701.
  • Qian P, Zhao K, Jiang Y, et al. Knowledge-leveraged transfer fuzzy c-means for texture image segmentation with self-adaptive cluster prototype matching. Knowl-Based Syst. 2017;130:33–50.
  • Li C, Wang X, Li J, et al. Joint probabilistic model of shape and intensity for multiple abdominal organ segmentation from volumetric ct images. IEEE J Biome Health Inf. 2013;17:92–102.
  • Selver MA. Segmentation of abdominal organs from ct using a multi-level, hierarchical neural network strategy. Comput Meth Prog Bio. 2014;113:830–852.
  • Lu J, Shi L, Deng M, et al. An interactive approach to liver segmentation in ct based on deformable model integrated with attractor force. In: Machine Learning and Cybernetics (ICMLC), 2011 International Conference on; Vol. 4; IEEE; 2011. p. 1660–1665.
  • Yang X, Yu HC, Choi Y, et al. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput Methods Programs Biomed. 2014;113:69–79.
  • Kohlberger T, Uzunba ¸ s MG, Alvino C, et al. Organ segmentation with level sets using local shape and appearance priors. In: International conference on medical image computing and computer-assisted intervention. Berlin, (Germany): Springer; 2009. p. 34–42.
  • Zhang X, Tian J, Deng K, et al. Automatic liver segmentation using a statistical shape model with optimal surface detection. IEEE Trans Biomed Eng. 2010;57:2622–2626.
  • Tomoshige S, Oost E, Shimizu A, et al. A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast ct images. Med Image Anal. 2014;18:130–143.
  • Zhou X, Kitagawa T, Hara T, et al. Constructing a probabilistic model for automated liver region segmentation using non-contrast x-ray torso ct images. In: International conference on medical image computing and computer-assisted intervention. Berlin, (Germany): Springer; 2006. p. 856–863.
  • Catt ´ e F, Lions PL, Morel JM, et al. Image selective smoothing and edge detection by nonlinear diffusion. SIAM J Numer Anal. 1992;29:182–193.
  • Oda M, Nakaoka T, Kitasaka T, et al. Organ segmentation from 3d abdominal ct images based on atlas selection and graph cut. In: International MICCAI workshop on computational and clinical challenges in abdominal imaging. Berlin, (Germany): Springer; 2011. p. 181–188.
  • Chu C, Oda M, Kitasaka T, et al. Multi-organ segmentation from 3d abdominal ct images using patient-specific weighted-probabilistic atlas. In: Medical Imaging 2013: Image Processing; Vol. 8669; International Society for Optics and Photonics; 2013. p. 86693Y.
  • Li C, Xu C, Gui C, et al. Distance regularized level set evolution and its application to image segmentation. IEEE T Image Process. 2010;19:3243.
  • Zhang K, Zhang L, Song H, et al. Active contours with selective local or global segmentation: a new formulation and level set method. Image Vis Comput. 2010;28:668–676.
  • Chan TF, Sandberg BY, Vese LA. Active contours without edges for vector-valued images. J Vis Commun Image R. 2000;11:130–141.
  • Li G, Chen X, Shi F, et al. Automatic liver segmentation based on shape constraints and deformable graph cut in ct images. IEEE T Image Process. 2015;24:5315–5329.
  • Erdt M, Steger S, Kirschner M, et al. Fast automatic liver segmentation combining learned shape priors with observed shape deviation. In: International Symposium on Computer-Based Medical Systems; IEEE; 2010. p. 249–254.
  • Linguraru MG, Sandberg JK, Li Z, et al. Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation In: international conference on medical image computing and computer-assisted intervention. Berlin, (Germany): Springer; 2009. p. 1001–1008.