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Multilabel statistical shape prior for image segmentation

Multilabel statistical shape prior for image segmentation

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Statistical shape models have been widely used to guide the segmentation in an image, thus overcoming noise and occlusions. In this study, the authors present a graph cut-based segmentation framework, in which multiple objects can be segmented. They design a specific multilabel shape prior, which is integrated into the graph cost function. They also want to enforce spatial constraint between the objects. Towards this aim, they propose a local constraint to forbid the inclusion of an object into another, which is enforced in the regularisation term of the graph energy. They apply the authors’ method to cardiac magnetic resonance images, in which left and right ventricles, and the myocardium are segmented and for which encouraging results are obtained.

References

    1. 1)
      • 1. Cootes, T.F., Taylor, C.J., Cooper, D.H., et al: ‘Active shape models-their training and application’, Comput. Vis. Image Underst., 1995, 61, (1), pp. 3859.
    2. 2)
      • 2. Leventon, M., Grimson, W., Faugeras, O.: ‘Statistical shape influence in geodesic active contours’. Computer Vision and Pattern Recognition, CVPR, 2000, vol. 1, pp. 316323.
    3. 3)
      • 3. Tsai, A., Yezzi, A.Jr., Wells, W., et al: ‘A shape-based approach to the segmentation of medical imagery using level sets’, IEEE Trans. Med. Imaging, 2003, 22, (2), pp. 137154.
    4. 4)
      • 4. Woo, J., Slomka, P.J., Kuo, C.-C.J., et al: ‘Multiphase segmentation using an implicit dual shape prior: application to detection of left ventricle in cardiac mri’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 10841094.
    5. 5)
      • 5. Song, Z., Tustison, N., Avants, B., et al: ‘Adaptive graph cuts with tissue priors for brain mri segmentation’. IEEE Int. Symp. on Biomedical Imaging (ISBI), 2006, pp. 762765.
    6. 6)
      • 6. Zhu-Jacquot, J., Zabih, R.: ‘Segmentation of the left ventricle in cardiac mr images using graph cuts with parametric shape priors’. Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 2008, pp. 521524.
    7. 7)
      • 7. Bai, W., Shi, W., Ledig, C., et al: ‘Multi-atlas segmentation with augmented features for cardiac MR images’, Med. Image Anal., 2015, 19, (1), pp. 98109.
    8. 8)
      • 8. Boykov, Y., Jolly, M.-P.: ‘Interactive graph cuts for optimal boundary & region segmentation of objects in nd images’. Int. Conf. on Computer Vision, ICCV, 2001, vol. 1, pp. 105112.
    9. 9)
      • 9. Mahapatra, D.: ‘Cardiac image segmentation from cine cardiac mri using graph cuts and shape priors’, J. Digital Imaging, 2013, 26, (4), pp. 721730.
    10. 10)
      • 10. Wang, H., Zhang, H., Ray, N.: ‘Adaptive shape prior in graph cut image segmentation’, Pattern Recognition, 2013, 46, pp. 14091414.
    11. 11)
      • 11. Das, P., Veksler, O., Zavadsky, V., et al: ‘Semiautomatic segmentation with compact shape prior’, Image Vis. Comput., 2009, 27, (1), pp. 206219.
    12. 12)
      • 12. Veksler, O.: ‘Star shape prior for graph-cut image segmentation’. Computer Vision–ECCV 2008, 2008, pp. 454467.
    13. 13)
      • 13. Funka-Lea, G., Boykov, Y., Florin, C., et al: ‘Automatic heart isolation for ct coronary visualization using graph-cuts’. Third IEEE Int. Symp. on Biomedical Imaging: Nano to Macro, 2006, 2006, pp. 614617.
    14. 14)
      • 14. Slabaugh, G., Unal, G.: ‘Graph cuts segmentation using an elliptical shape prior’. IEEE Int. Conf. on Image Processing, 2005, vol. 2, p. II1222.
    15. 15)
      • 15. Freedman, D., Zhang, T.: ‘Interactive graph cut based segmentation with shape priors’. Computer Vision and Pattern Recognition, CVPR, vol. 1, 2005, pp. 755762.
    16. 16)
      • 16. Vu, N., Manjunath, B.S.: ‘Shape prior segmentation of multiple objects with graph cuts’. Computer Vision and Pattern Recognition, CVPR, 2008, pp. 18.
    17. 17)
      • 17. Chen, X., Bagci, U.: ‘3d automatic anatomy segmentation based on iterative graph-cut-asm’, Med. Phys., 2011, 38, p. 4610.
    18. 18)
      • 18. Delong, A., Gorelick, L., Schmidt, F., et al: ‘Interactive segmentation with super-labels’. Energy Minimization Methods in Computer Vision and Pattern Recognition, 2011, pp. 147162.
    19. 19)
      • 19. Shimizu, A., Nakagomi, K., Narihira, T., et al: ‘Automated segmentation of 3d ct images based on statistical atlas and graph cuts’. Medical Computer Vision Recognition Techniques and Applications in Medical Imaging, 2011, pp. 214223.
    20. 20)
      • 20. Vicente, S., Kolmogorov, V., Rother, C.: ‘Graph cut based image segmentation with connectivity priors’. Computer Vision and Pattern Recognition, CVPR, June 2008, pp. 18.
    21. 21)
      • 21. Haddad, F., Hunt, S., Rosenthal, D., et al: ‘Right ventricular function in cardio-vascular disease, part i anatomy, physiology, aging, functional assessment of the right ventricle’, Circulation, 2008, 117, (11), pp. 14361448.
    22. 22)
      • 22. Mitchell, S., Lelieveldt, B., van der Geest, R., et al: ‘Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac mr images’, IEEE Trans. Med. Imaging., 2001, 20, (5), pp. 415423.
    23. 23)
      • 23. Ordas, S., Boisrobert, L., Huguet, M., et al: ‘Active shape models with invariant optimal features (iof-asm) application to cardiac mri segmentation’. IEEE Computers in Cardiology, 2003, 2003, pp. 633636.
    24. 24)
      • 24. Kirisli, H., Schaap, M., Klein, S., et al: ‘Fully automatic cardiac segmentation from 3d cta data: a multi-atlas based approach’. Proc. of SPIE, vol. 7623, 2010, p. 7623051.
    25. 25)
      • 25. Moolan-Feroze, O., Mirmehdi, M., Hamilton, M., et al: ‘Segmentation of the right ventricle using diffusion maps and markov random fields’. Medical Image Computing and Computer-Assisted Intervention, 2014, pp. 682689.
    26. 26)
      • 26. Grosgeorge, D., Petitjean, C., Ruan, S.: ‘Joint segmentation of right and left cardiac ventricles using multi-label graph cut’. Int. Symp. on Biomedical Imaging (ISBI), April 2014, pp. 429432.
    27. 27)
      • 27. Cocosco, C., Netsch, T., Sénégas, J., et al: ‘Automatic cardiac region-of-interest computation in cine 3d structural mri’. Computer Assisted Radiology and Surgery, vol. 1268, 2004, pp. 11261131.
    28. 28)
      • 28. Tang, T., Chung, A.: ‘Non-rigid image registration using graph-cuts’. Medical Image Computing and Computer-Assisted Intervention, 2007, pp. 916924.
    29. 29)
      • 29. So, R., Chung, A.: ‘Multi-level non-rigid image registration using graph-cuts’. Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, 2009, pp. 397400.
    30. 30)
      • 30. So, R., Chung, A.: ‘Non-rigid image registration by using graph-cuts with mutual information’. Int. Conf. on Image Processing, ICIP, 2010, pp. 44294432.
    31. 31)
      • 31. Chowdhury, A., Roy, R., Bose, S., et al: ‘Non-rigid biomedical image registration using graph cuts with a novel data term’. IEEE Int. Symp. on Biomedical Imaging (ISBI), 2012, pp. 446449.
    32. 32)
      • 32. Boykov, Y., Veksler, O., Zabih, R.: ‘Efficient approximate energy minimization via graph cuts’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (11), pp. 12221239.
    33. 33)
      • 33. Bai, W., Shi, W., O'Regan, D., et al: ‘A probabilistic patch-based label fusion model for multi-atlas segmentation with registration refinement: application to cardiac mr images’, IEEE Trans. Med. Imaging, 2013, 32, (7), pp. 13021315.
    34. 34)
      • 34. Kolmogorov, V., Zabin, R.: ‘What energy functions can be minimized via graph cuts?’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (2), pp. 147159.
    35. 35)
      • 35. Malcolm, J., Rathi, Y., Tannenbaum, A.: ‘Graph cut segmentation with nonlinear shape priors’. Int. Conf. on Image Processing, ICIP, 2007, vol. 4, p. 365368.
    36. 36)
      • 36. Petitjean, C., Ruan, S., Grosgeorge, D., et al: ‘Right ventricle segmentation in cardiac mri: a miccai'12 challenge’. Proc. of 3D Cardiovascular Imaging: a MICCAI Segmentation Challenge, 2012.
    37. 37)
      • 37. Boykov, Y., Kolmogorov, V.: ‘An experimental comparison of min-cut/max-ow algorithms for energy minimization in vision’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (9), pp. 11241137.
    38. 38)
      • 38. Petitjean, C., Dacher, J.-N.: ‘A review of segmentation methods in short axis cardiac mr images’, Med. Image Anal., 2011, 15, (2), pp. 169184.
    39. 39)
      • 39. Petitjean, C., Zuluaga, M.A., Bai, W., et al: ‘Right ventricle segmentation from cardiac mri: a collation study’, Med. Image Anal., 2015, 19, pp. 187202.
    40. 40)
      • 40. Zuluaga, M., Cardoso, M., Modat, M., et al: ‘Multi-atlas propagation whole heart segmentation from mri and cta using a local normalised correlation coefficient criterion’. Functional Imaging and Modeling of the Heart, 2013, pp. 174181.
    41. 41)
      • 41. Ou, Y., Sotiras, A., Paragios, N., et al: ‘Dramms: deformable registration via attribute matching and mutual-saliency weighting’, Med. Image Anal., 2011, 15, (4), pp. 622639.
    42. 42)
      • 42. Wang, C.-W., Peng, C.-W., Chen, H.-C.: ‘A simple and fully automatic right ventricle segmentation method for 4-dimensional cardiac mr images’. Proc. of 3D Cardiovascular Imaging: a MICCAI Segmentation Challenge, 2012.
    43. 43)
      • 43. Maier, O., Jiménez, D., Santos, A., et al: ‘Segmentation of rv in 4d cardiac mr volumes using region-merging graph cuts’. Computing in Cardiology, 2012, pp. 697700.
    44. 44)
      • 44. Grosgeorge, D., Petitjean, C., Dacher, J.-N., et al: ‘Graph cut segmentation with a statistical shape model in cardiac mri’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 10271035.
    45. 45)
      • 45. Schmidt, M., Alahari, K.: ‘Generalized fast approximate energy minimization via graph cuts: Alpha-expansion beta-shrink moves’. Proc. of Conf. on Uncertainty in Artificial Intelligence, 2011.
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