A new contouring method for producing region boundaries in two-dimensional (2D) scalar-value image datasets (such as grey-scale intensity images from a digital camera or X-ray device) with sub-pixel precision is introduced here. The method, fine feature sensitive marching squares (FFS-MS), extends MS isocontouring to produce an isocontour that preserves fine-scale features (which are often incorrectly recovered by standard MS). This extension is the 2D analogue of Kaneko and Yamamoto's volume preserving marching cubes algorithm. It has several phases. First, it recovers an isocontour using standard MS. Then, it produces a new dataset with data values estimated by treating the recovered contour as the actual boundary. Using this new dataset, it next compares that dataset's estimated data values with the data values at corresponding locations in the original dataset. Finally, the method adjusts the original dataset's pixel values at every pixel location, where there is a high discrepancy between the original data value and the estimated data value. It iteratively repeats its phases until an optimality criterion is satisfied. Experimental analyses of FFS-MS are also presented. The analyses focus on FF recovery in comparison with the standard MS.

References

1. 1)
  - 7. Olsen, S., Gooch, B.: ‘Image simplification and vectorization’. Proc. ACM SIGGRAPH/EG Symp. Non-Photorealistic Animation and Rendering, 2011, pp. 65–74.
2. 2)
  - 17. Bestman, J., Cline, H.: ‘Xenopus laevis’. Available at http://www.cellimagelibrary.org/images?k=neuronpage=8simplesearch=Search, accessed 20 September 2016.
3. 3)
  - 6. Müller, M.: ‘Fast and robust tracking of fluid surfaces’. Proc. ACM SIGGRAPH/EG Symp. Computer Animation (SCA) ’09, 2009, pp. 237–245.
4. 4)
  - 2. Achanta, R., Shaji, A., Smith, K., et al: ‘SLIC superpixels compared to state-of-the-art superpixel methods’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (11), pp. 2274–2282.
5. 5)
  - 16. Swanowicz, D.I.: ‘Antenna of a male moth’. Available at http://www.nikonsmallworld.com/people/image/dr.-igor-siwanowicz/4, accessed 20 September 2016.
6. 6)
  - 12. Newman, T., Yi, H.: ‘A survey of the marching cubes algorithm’, Comput. Graph., 2006, 30, (5), pp. 854–879.
7. 7)
  - 11. Müller, M., Schirm, S., Duthaler, S.: ‘Screen space meshes’. Proc. ACM SIGGRAPH/EG Symp. Computer Animation (SCA) ’07, 2007, pp. 9–15.
8. 8)
  - 1. Freeman, H.: ‘Computer processing of line-drawing images’, ACM Comput. Surv., 1974, 6, (1), pp. 57–97.
9. 9)
  - 8. Ho, C., Wu, F., Chen, B., et al: ‘Cubical marching squares: adaptive feature preserving surface extraction from volume data’, Comput. Graph. Forum (CGF), 2005, 24, (3), pp. 537–545.
10. 10)
  - 5. Cipolletti, M., Delrieux, C., Perillo, G., et al: ‘Super-resolution border segmentation and measurement in remote sensing images’, Comput. Geosci., 2012, 40, pp. 79–87.
11. 11)
  - 10. Schlei, B.: ‘A new computational framework for 2d shape-enclosing contours’, Image Vis. Comput., 2009, 27, pp. 637–647.
12. 12)
  - 15. Kaneko, T., Yamamoto, Y.: ‘Volume-preserving surface reconstruction from volume data’. Proc. Int. Conf. Image Processing ’97, 1997, vol. 1, pp. 583–598.
13. 13)
  - 9. Edmunds, M., Laramee, R., Chen, G., et al: ‘Advanced automatic stream surface seeding and filtering’, in Carr, H., Czanner, S. (EDs.): ‘Theory and practice computer graphics’ (The Eurographics Association, 2012).
14. 14)
  - 3. Yarkony, J., Zhang, C., Fowlkes, C.C.: ‘Hierarchical planar correlation clustering for cell segmentation’. Proc. Int. Work on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2015, pp. 492–504.
15. 15)
  - 13. Gong, S., Newman, T.: ‘A corner feature sensitive marching squares’, Proc. IEEE SOUTHEASTCON, 2013, 2013, pp. 1–6.
16. 16)
  - 14. Gong, S., Newman, T.: ‘Dual marching squares: description and analysis’. Proc. 2016 IEEE Southwest Symp. Image Analysis and Interpretation (SSIAI), 2016, pp. 53–56.
17. 17)
  - 4. Krishnan, K.R., Radhakrishnan, S.: ‘Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation’, IET Image Process., 2015, 9, (4), pp. 261–270.

Fine feature sensitive marching squares

References

Related content