access icon free Adaptive localised region and edge-based active contour model using shape constraint and sub-global information for uterine fibroid segmentation in ultrasound-guided HIFU therapy

Uterine fibroids segmentation in ultrasound images is of great importance in the definition of intra-operative planning of ultrasound-guided high-intensity focused ultrasound (HIFU) therapy. However, it is challenging to obtain accurate, robust and efficient uterine fibroid segmentation due to low quality of ultrasound images. In this study, the authors propose a novel adaptive localised region and edge-based active contour model using shape constraint and sub-global information to accurately and efficiently segment the uterine fibroids in ultrasound images with robustness against initial contour. The authors first define adaptive local radius for the localised region-based model and combine it with the edge-based model to accurately and efficiently capture image's heterogeneous features and edge features. Then, they incorporate a shape constraint to reduce boundary leakage or excessive contraction to obtain more accurate segmentation. To overcome the initialisation sensitivity, they introduce the sub-global information to prevent the curve from trapping into the local minima and obtain robust results. Furthermore, the authors optimise computation by adaptively sharing local region and employing the multi-scale segmentation method to achieve efficient segmentation. The proposed method is validated by uterine fibroid ultrasound images in HIFU therapy and the results demonstrate that it can achieve accurate, robust and efficient segmentation.

Inspec keywords: image segmentation; medical image processing; ultrasonic imaging

Other keywords: adaptive localised region; intraoperative planning; uterine fibroid segmentation; localised region-based model; multiscale segmentation method; image heterogeneous features; ultrasound-guided HIFU therapy; adaptive local radius; boundary leakage; edge-based active contour model; ultrasound images; ultrasound-guided high-intensity focused ultrasound therapy; initialisation sensitivity; shape constraint; edge features; subglobal information

Subjects: Sonic and ultrasonic radiation (biomedical imaging/measurement); Sonic and ultrasonic applications; Optical, image and video signal processing; Computer vision and image processing techniques; Patient diagnostic methods and instrumentation

References

    1. 1)
      • 23. Sum, K., Cheung, P.: ‘Vessel extraction under non-uniform illumination: a level set approach’, IEEE Trans. Biomed. Eng., 2008, 55, (1), pp. 358360.
    2. 2)
      • 22. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266277.
    3. 3)
      • 17. Kichenassamy, S., Kumar, A., Olver, P., et al: ‘Conformal curvature flows: from phase transitions to active vision’, Arch. Ration. Mech. Anal., 1996, 134, (3), pp. 275301.
    4. 4)
      • 30. Tran, T.T., Pham, V.T., Shyu, K.K.: ‘Image segmentation using fuzzy energy-based active contour with shape prior’, J. Vis. Commun. Image Represent., 2014, 25, (25), pp. 17321745.
    5. 5)
      • 13. Cheng, H.D., Shan, J., Ju, W., et al: ‘Automated breast cancer detection and classification using ultrasound images: a survey’, Pattern Recognit., 2010, 43, (1), pp. 299317.
    6. 6)
      • 3. Kennedy, J.E.: ‘High-intensity focused ultrasound in the treatment of solid tumours’, Nat. Rev. Cancer, 2005, 5, (4), pp. 321327.
    7. 7)
      • 2. Elizabeth, A., Stewart, M.D.: ‘Uterine fibroids’, N. Engl. J. Med., 2015, 372, (17), pp. 16461655.
    8. 8)
      • 7. Garnier, C., Bellanger, J.J., Wu, K., et al: ‘Prostate segmentation in HIFU therapy’, IEEE Trans. Med. Imaging, 2011, 30, (3), pp. 792803.
    9. 9)
      • 8. Xu, M., Zhang, D., Yang, Y., et al: ‘A split-and-merge-based uterine fibroid ultrasound image segmentation method in HIFU therapy’, PLoS ONE, 2015, 10, (5), p. e0125738.
    10. 10)
      • 20. Xu, C., Prince, J.L.: ‘Snakes, shapes, and gradient vector flow’, IEEE Trans. Image Process., 1998, 7, (3), pp. 359369.
    11. 11)
      • 5. Fontanarosa, D., van der Meer, S., Bamber, J., et al: ‘Review of ultrasound image guidance in external beam radiotherapy: I. Treatment planning and inter-fraction motion management’, Phys. Med. Biol., 2015, 60, (3), p. R77.
    12. 12)
      • 28. Tian, Y., Duan, F., Zhou, M., et al: ‘Active contour model combining region and edge information’, Mach. Vis. Appl., 2013, 24, (1), pp. 4761.
    13. 13)
      • 24. Brox, T., Cremers, D.: ‘On the statistical interpretation of the piecewise smooth Mumford–Shah functional’. Proc. Scale Space and Variational Methods in Computer Vision, 2007, pp. 203213.
    14. 14)
      • 16. Kass, M., Witkin, A., Terzopoulos, D.: ‘Snakes: active contour models’, Int. J. Comput. Vis., 1988, 1, (4), pp. 321331.
    15. 15)
      • 12. Wang, H., Huang, T.Z., Xu, Z., et al: ‘An active contour model and its algorithms with local and global Gaussian distribution fitting energies’, Inf. Sci., 2014, 263, pp. 4359.
    16. 16)
      • 18. Ronfard, R.: ‘Region-based strategies for active contour models’, Int. J. Comput. Vis., 1994, 13, (2), pp. 229251.
    17. 17)
      • 25. Lankton, S., Tannenbaum, A.: ‘Localizing region-based active contours’, IEEE Trans. Image Process., 2008, 17, (11), pp. 20292039.
    18. 18)
      • 26. Li, C., Kao, C., Gore, J.C., et al: ‘Implicit active contours driven by local binary fitting energy’. IEEE Conf. on Computer Vision and Pattern Recognition, Washington, DC, 2007, pp. 17.
    19. 19)
      • 31. Liao, X., Yuan, Z., Zheng, Q., et al: ‘Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy’, PLoS ONE, 2014, 9, (7), p. e103334, doi: 10.1371/journal.pone.0103334 PMID: 25061939.
    20. 20)
      • 32. Dietenbeck, T., Alessandrini, M., Friboulet, D., et al: ‘CREASEG: a free software for the evaluation of image segmentation algorithms based on level set’. IEEE Int. Conf. on Image Processing, Hong Kong, China, 2010, pp. 665668.
    21. 21)
      • 1. Walker, C.L., Stewart, E.A.: ‘Uterine fibroids: the elephant in the room’, Science, 2005, 308, (5728), pp. 15891592.
    22. 22)
      • 27. 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, (4), pp. 668676.
    23. 23)
      • 4. Zhang, L., Zhu, H., Jin, C., et al: ‘High-intensity focused ultrasound (HIFU): effective and safe therapy for hepatocellular carcinoma adjacent to major hepatic veins’, Eur. Radiol., 2009, 19, (2), pp. 437445.
    24. 24)
      • 10. Smistad, E., Falch, T.L., Bozorgi, M., et al: ‘Medical image segmentation on GPUs – a comprehensive review’, Med. Image Anal., 2015, 20, (1), pp. 118.
    25. 25)
      • 15. Wang, W., Zhu, L., Qin, J., et al: ‘Multiscale geodesic active contours for ultrasound image segmentation using speckle reducing anisotropic diffusion’, Opt. Lasers Eng., 2014, 54, pp. 105116.
    26. 26)
      • 14. Li, C., Huang, R., Ding, Z., et al: ‘A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI’, IEEE Trans. Image Process., 2011, 20, (7), pp. 20072016.
    27. 27)
      • 21. Niu, S., Chen, Q., de Sisternes, L., et al: ‘Robust noise region-based active contour model via local similarity factor for image segmentation’, Pattern Recognit., 2016, 21, pp. 104119.
    28. 28)
      • 6. Noble, J.A., Boukerroui, D.: ‘Ultrasound image segmentation: a survey’, IEEE Trans. Med. Imaging, 2006, 25, (8), pp. 9871010.
    29. 29)
      • 11. Ghose, S., Oliver, A., Martí, R., et al: ‘A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images’, Comput. Methods Programs Biomed., 2012, 108, (1), pp. 262287.
    30. 30)
      • 9. Menze, B.H., Jakab, A., Bauer, S., et al: ‘The multimodal brain tumor image segmentation benchmark (BRATS)’, IEEE Trans. Med. Imaging, 2015, 34, (10), pp. 19932024.
    31. 31)
      • 19. Caselles, V., Kimmel, R., Sapiro, G.: ‘Geodesic active contours’, Int. J. Comput. Vis., 1997, 22, (1), pp. 6179.
    32. 32)
      • 29. Wu, Y., Wang, Y., Jia, Y.: ‘Segmentation of the left ventricle in cardiac cine MRI using a shape-constrained snake model’, Comput. Vis. Image Underst., 2013, 117, (9), pp. 9901003.
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