access icon free Adaptive active contours based on variable kernel with constant initialisation

© The Institution of Engineering and Technology
Received 16/05/2017, Accepted 03/02/2018, Revised 20/01/2018, Published 12/02/2018

In this paper, a novel method of active contours based on the formulation of partial differential equation (PDE) is proposed for image segmentation. The evolution equation incorporates a force term that pushes the contour towards object boundary, a regularisation term which takes into account the smoothness of the level set function and an edge term which helps to stop the contour at required boundaries. The proposed method integrates an image convolved by a variable kernel into an energy formulation, where the width of the kernel varies in each iteration. Therefore, it takes local region information when the width of the kernel is small while for the larger width of the kernel, the proposed method considers global region information across the regions. Due to the use of both local and global image information, the method easily detects objects in the complex background and also segments the objects where intensity changes within the object. Moreover, the proposed method totally eliminates the need of the contour initialisation by using constant initialisation scheme. Experimental results on real and medical images prove the robustness of the proposed method. Finally, the authors validate their method on PH2 database for skin lesion segmentation.

Inspec keywords: cancer; image segmentation; partial differential equations; skin; set theory; object detection; medical image processing

Other keywords: local region information; evolution equation; complex background; constant initialisation scheme; level set function smoothness; PH2 database; object boundary; iteration method; disease diagnosis; edge term; skin lesion segmentation; partial differential equation; energy formulation; PDE; medical image; image segmentation; global image information; object detection; regularisation term; active contour methods; adaptive active contours; local image information; variable kernel

Subjects: Mathematical analysis; Mathematical analysis; Algebra, set theory, and graph theory; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Optical, image and video signal processing; Combinatorial mathematics; Computer vision and image processing techniques; Combinatorial mathematics; Biomedical measurement and imaging; Biology and medical computing; Patient diagnostic methods and instrumentation

References

    1. 1)
      • 2. Kass, M., Witkin, A., Terzopoulos, D.: ‘Snakes: active contour models’, Int. J. Comput. Vis., 1988, 1, (4), pp. 321–331.
    2. 2)
      • 5. Caselles, V., Catte, F., Coll, T., et al: ‘A geometric model for active contours in image processing’, Numer. Math., 1993, 66, (1), pp. 1–31.
    3. 3)
      • 18. Wang, H., Huang, T.Z.: ‘Region-based object and background extraction via active contours’, Optik – Int. J. Light and Electron Optics, 2013, 124, (23), pp. 6020–6026.
    4. 4)
      • 14. Vese, L.A., Chan, T.F.: ‘A multiphase level set framework for image segmentation using the Mumford and Shah model’, Int. J. Comput. Vis., 2002, 50, (3), pp. 271–293.
    5. 5)
      • 11. Chan, T.F., Vese, L.A.: ‘Active contours without edges’, IEEE Trans. Image Process., 2001, 10, (2), pp. 266–277.
    6. 6)
      • 16. Lankton, S., Tannenbaum, A.: ‘Localizing region-based active contours’, IEEE Trans. Image Process., 2008, 17, (11), pp. 2029–2039.
    7. 7)
      • 32. Barata, C., Celebi, M.E., Marques, J.S.: ‘Melanoma detection algorithm based on feature fusion’. 37th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), August 2015, pp. 2653–2656.
    8. 8)
      • 29. Yao, T., Wang, Z., Xie, Z., et al: ‘A multiview joint sparse representation with discriminative dictionary for melanoma detection’. Int. Conf. on Digital Image Computing: Techniques and Applications (DICTA), November 2016, pp. 1–6.
    9. 9)
      • 23. Wang, Y., He, C.: ‘Adaptive level set evolution starting with a constant function’. Appl. Math. Model., 2012, 36, (7), pp. 3217–3228.
    10. 10)
      • 1. Osher, S., Sethian, J.A.: ‘Fronts propagating with curvature-dependent speed: algorithms based on hamilton-jacobi formulations’, J. Comput. Phys., 1988, 79, (1), pp. 12–49.
    11. 11)
      • 25. Akram, F., Garcia, M.A., Puig, D.: ‘Active contours driven by local and global fitted image models for image segmentation robust to intensity inhomogeneity’, PloS one, 2017, 12, (4), pp. 1–34.
    12. 12)
      • 6. Chunming, L., Chenyang, X., Changfeng, G., et al: ‘Level Set evolution without Re-initialization: A New variational formulation’. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, June 2005, vol. 1, pp. 430–436.
    13. 13)
      • 8. Chunming, L., Chenyang, X., Changfeng, G., et al: ‘Distance regularized level set evolution and its application to image segmentation’, IEEE Trans. Image Process., 2010, 19, (12), pp. 3243–3254.
    14. 14)
      • 26. Zhang, L., Peng, X., Li, G., et al: ‘A novel active contour model for image segmentation using local and global region-based information’, Mach. Vis. Appl., 2017, 28, (1-2), pp. 75–89.
    15. 15)
      • 22. Mumford, D., Shah, J.: ‘Optimal approximations by piecewise smooth functions and associated variational problems’, Commun. Pure Appl. Math., 1989, 42, (5), pp. 577–685.
    16. 16)
      • 21. El-Rewaidy, H., Ibrahim, E.S., Fahmy, A.S.: ‘Segmentation of the right ventricle in MRI images using a dual active shape model’, IET Image Process., 2016, 10, (10), pp. 717–723.
    17. 17)
      • 30. Barata, C., Ruela, M., Francisco, M., et al: ‘Two systems for the detection of melanomas in dermoscopy images using texture and color features’, IEEE Syst. J., 2014, 8, (3), pp. 965–979.
    18. 18)
      • 9. Caselles, V., Kimmel, R., Sapiro, G.: ‘Geodesic active contours’, Int. J. Comput. Vis., 1997, 22, (1), pp. 61–79.
    19. 19)
      • 19. Jiang, X., Li, B., Wang, Q., et al: ‘A novel active contour model driven by local and global intensity fitting energies’, Optik – Int. J. Light and Electron Optics, 2014, 125, (21), pp. 6445–6449.
    20. 20)
      • 10. Kim, J.H., Park, B.Y., Akram, F., et al: ‘Multipass active contours for an adaptive contour map’, Sensors, 2013, 13, (3), pp. 3724–3738.
    21. 21)
      • 3. Hussain, S., Chun, Q., Asif, M.R., et al: ‘Active contours for image segmentation using complex domain-based approach’, IET Image Process., 2016, 10, (2), pp. 121–129.
    22. 22)
      • 4. Goldenberg, R., Kimmel, R.: ‘Fast geodesic active contours’, IEEE Trans. Image Process., 2001, 10, (10), pp. 1467–1475.
    23. 23)
      • 12. Li, C., Kao, C.Y., Gore, J.C., et al: ‘Minimization of region-scalable fitting energy for image segmentation’, IEEE Trans. Image Process., 2008, 17, (10), pp. 1940–1949.
    24. 24)
      • 24. Wen, W.: ‘Adaptive active contours based on local and global intensity information for image segmentation’, Optik-Int. J. Light and Electron Optics, 2014, 125, (23), pp. 6995–7001.
    25. 25)
      • 27. Soomro, S., Akram, F., Munir, A., et al: ‘Segmentation of left and right ventricles in cardiac MRI using active contours’, Comput. Math. Methods Med., 2017, 2017, pp. 1–16.
    26. 26)
      • 17. Peng, Y., Liu, F., Liu, S.: ‘Active contours driven by normalized local image fitting energy’, Concurrency Comput. Pract. Exp., 2014, 26, (5), pp. 1200–1214.
    27. 27)
      • 20. Soomro, S., Akram, F., Kim, J.H., et al: ‘Active contours using additive local and global intensity fitting models for intensity inhomogeneous image segmentation’, Comput. Math. Methods Med., 2016, 2016, pp. 1–15.
    28. 28)
      • 15. 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. 668–676.
    29. 29)
      • 13. Wang, L., Li, C., Sun, Q., et al: ‘Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation’, Comput. Med. Imaging Graph., 2009, 33, (7), pp. 520–531.
    30. 30)
      • 7. Zhao, P., Ren, H.E., Pu, Z.B.: ‘Simultaneous surface area measurement for multiple objects by geodesic active contour’, Optik – Int. J. Light Electron Optics, 2009, 120, (10), pp. 484–489.
    31. 31)
      • 33. Mendonça, T., Ferreira, P.M., Marques, J.S., et al: ‘PH 2-A dermoscopic image database for research and benchmarking’. 35th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC), July 2013, pp. 5437–5440.
    32. 32)
      • 31. Riaz, F., Hassan, A., Javed, M.Y., et al: ‘Detecting melanoma in dermoscopy images using scale adaptive local binary patterns’. 36th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society, August 2014, pp. 6758–6761.
    33. 33)
      • 28. Aubert, G., Kornprobst, P.: ‘Mathematical problems in image processing: partial differential equations and the calculus of variations’ (Springer-Verlag, New York, 2006, 2nd edn.), 147.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2017.0481
Loading

Related content

content/journals/10.1049/iet-ipr.2017.0481
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading