Pearling: Stroke Segmentation with Crusted Pearl Strings

B. Whited, J. Rossignac, G. Slabaugh, T. Fang, G. Unal

2008

Abstract

We introduce a novel segmentation technique, called Pearling, for the semi-automatic extraction of idealized models of networks of strokes (variable width curves) in images. These networks may for example represent roads in an aerial photograph, vessels in a medical scan, or strokes in a drawing. The operator seeds the process by selecting representative areas of good (stroke interior) and bad colors. Then, the operator may either provide a rough trace through a particular path in the stroke graph or simply pick a starting point (seed) on a stroke and a direction of growth. Pearling computes in realtime the centerlines of the strokes, the bifurcations, and the thickness function along each stroke, hence producing a purified medial axis transform of a desired portion of the stroke graph. No prior segmentation or thresholding is required. Simple gestures may be used to trim or extend the selection or to add branches. The realtime performance and reliability of Pearling results from a novel disk-sampling approach, which traces the strokes by optimizing the positions and radii of a discrete series of disks (pearls) along the stroke. A continuous model is defined through subdivision. By design, the idealized pearl string model is slightly wider than necessary to ensure that it contains the stroke boundary. A narrower core model that fits inside the stroke is computed simultaneously. The difference between the pearl string and its core contains the boundary of the stroke and may be used to capture, compress, visualize, or analyze the raw image data along the stroke boundary.

References

  1. C. Kirbas, F. Quek, A Review of Vessel Extraction Techniques and Algorithms, ACM Computing Surveys 36 (2) (2004) 81-121.
  2. J. A. Tyrrell, E. di Tomaso, D. Fuja, R. Tong, K. Kozak, R. Jain, B. Roysam, Robust 3-D Modeling of Vasculature Imagery Using Superellipsoids, IEEE Trans. on Medical Imaging 26 (2) (2007) 223-237.
  3. L. M. Lorigo, O. D. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikinis, A. Nabavi, C.- F. Westin, CURVES: Curve Evolution for Vessel Segmentation, Medical Image Analysis 5 (2001) 195-206.
  4. J. Soares, J. Leandro, R. Cesar, H. Jelinek, M. Cree, Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification, IEEE Trans. on Med. Img. 25 (9) (2006) 1214-1222.
  5. O. Wink, W. Niessen, M. A. Viergever, Fast delineation and visualization of vessels in 3-d angiographic images, IEEE Trans. on Medical Imaging 19 (4) (2000) 337-346.
  6. A. Szymczak, A. Tannenbaum, K. Mischaikow, Coronary vessel cores from 3D imagery: a topological approach, in: Medical Imaging 2005: Image Processing. Proceedings of the SPIE, Vol. 5747, 2005, pp. 505-513.
  7. P. Medek, P. Benes, J. Sochor, Computation of tunnels in protein molecules using Delaunay triangulation, in: Journal of WSCG, 2007, p. 8.
  8. L. Costa, R. Cesar, Shape Analysis and Classification, CRC Press, 2001.
  9. A. Telea, J. J. van Wijk, An augmented fast marching method for computing skeletons and centerlines, in: Symposium on Data Visualisation, 2002, pp. 251-259.
  10. C. Pudney, Distance-ordered homotopic thinning: A skeletonization algoritm for 3D digital images, IEEE. Trans. on Biomedical Engineering 72 (3) (1998) 404-413.
  11. M. Van Dortmont, H. van de Wetering, A. Telea, Skeletonization and distance transforms of 3d volumes using graphics hardware, in: DGCI, 2006, pp. 617-629.
  12. N. Cornea, D. Silver, X. Yuan, R. Balasubramanian, Computing hierarchical curve-skeletons of 3d objects, The Visual Computer 21 (11) (2005) 945-955.
  13. H. Li, A. Yezzi, Vessels as 4D Curves: Global Minimal 4D Paths to 3D Tubular Structure Extraction, in: Workshop on Mathematical Methods in Biomedical Image Analysis, 2006.
  14. L. D. Cohen, R. Kimmel, Global minimum for active contours models: A minimal path approach, IJCV 24 (1) (1997) 57-78.
  15. H. Blum, A Transformation for Extracting New Descriptors of Shape, in: W. Wathen-Dunn (Ed.), Models for the Perception of Speech and Visual Form, MIT Press, Cambridge, 1967, pp. 362-380.
  16. B. Whited, J. Rossignac, G. Slabaugh, T. Fang, G. Unal, Pearling: 3d interactive extraction of tubular structures from volumetric images, in: MICCAI Workshop: Interaction in Medical Image Analysis and Visualization, 2007.
  17. G. Monge, Applications de l'analyse à la géométrie, 5th Edition, Bachelier, Paris, 1894.
Download


Paper Citation


in Harvard Style

Whited B., Rossignac J., Slabaugh G., Fang T. and Unal G. (2008). Pearling: Stroke Segmentation with Crusted Pearl Strings . In Proceedings of the 1st International Workshop on Image Mining Theory and Applications IMTA 2008 - Volume 1: IMTA, (VISIGRAPP 2008) ISBN 978-989-8111-25-8, pages 103-112. DOI: 10.5220/0002340801030112


in Bibtex Style

@conference{imta08,
author={B. Whited and J. Rossignac and G. Slabaugh and T. Fang and G. Unal},
title={Pearling: Stroke Segmentation with Crusted Pearl Strings},
booktitle={Proceedings of the 1st International Workshop on Image Mining Theory and Applications IMTA 2008 - Volume 1: IMTA, (VISIGRAPP 2008)},
year={2008},
pages={103-112},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002340801030112},
isbn={978-989-8111-25-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Workshop on Image Mining Theory and Applications IMTA 2008 - Volume 1: IMTA, (VISIGRAPP 2008)
TI - Pearling: Stroke Segmentation with Crusted Pearl Strings
SN - 978-989-8111-25-8
AU - Whited B.
AU - Rossignac J.
AU - Slabaugh G.
AU - Fang T.
AU - Unal G.
PY - 2008
SP - 103
EP - 112
DO - 10.5220/0002340801030112