Segmentation of Cardiac Images by the Force Field Driven Speed Term
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33122
Segmentation of Cardiac Images by the Force Field Driven Speed Term

Authors: Renato Dedic, Madjid Allili, Roger Lecomte, Adbelhamid Benchakroun

Abstract:

The class of geometric deformable models, so-called level sets, has brought tremendous impact to medical imagery. In this paper we present yet another application of level sets to medical imaging. The method we give here will in a way modify the speed term in the standard level sets equation of motion. To do so we build a potential based on the distance and the gradient of the image we study. In turn the potential gives rise to the force field: F~F(x, y) = P ∀(p,q)∈I ((x, y) - (p, q)) |ÔêçI(p,q)| |(x,y)-(p,q)| 2 . The direction and intensity of the force field at each point will determine the direction of the contour-s evolution. The images we used to test our method were produced by the Univesit'e de Sherbrooke-s PET scanners.

Keywords: PET, Cardiac, Heart, Mouse, Geodesic, Geometric, Level Sets, Deformable Models, Edge Detection, Segmentation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1072698

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1216

References:


[1] V. Caselles, R. Kimmel, and G. Sapiro, Geodesic Active Contours, Int-l J. Computer Vision, vol. 22, pp. 61-79, 1997.
[2] V. Caselles, F. Catt'e, T. Coll, and F. Dibos, A geometric model for active contours in image processing, Numer. Math., vol. 66, pp. 1-31, 1993.
[3] R. Malladi, J.A. Sethian, and B.C. Vemuri, Shape Modeling with Front Propagation: A Level Set Approach, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 158-175, 1995.
[4] J. Sethian. Level Sets Methods and Fast Marching Methods. Cambridge University Press, 1999.
[5] S. Osher och R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer-Verlag, 2002.
[6] A. Rosenfeld and A.C. Kak, Digital Picture Processing(New York: Academic Press, 1982).
[7] S. M. Larie and S. S. Abukmeil, Brain abnormality in schizophrenia: a systematic and quantitative review of volumetric magnetic resonance imaging studies, J. Psych., vol. 172, pp. 110-120, 1998.
[8] P. Taylor, Invited review: computer aids for decision-making in diagnostic radiology -a literature review, Brit. J. Radiol., vol. 68, pp. 945-957, 1995.
[9] A. P. Zijdenbos and B. M. Dawant, Brain segmentation and white matter lesion detection in MR images, Critical Reviews in Biomedical Engineering, vol. 22, pp. 401-465, 1994.
[10] A. J. Worth, N. Makris, V. S. Caviness, and D. N. Kennedy, Neuroanatomical segmentation in MRI: technological objectives, Int-l J. Patt. Recog. Artificial Intell., vol. 11, pp. 1161-1187, 1997.
[11] C. A. Davatzikos and J. L. Prince, An active contour model for mapping the cortex, IEEE Trans. Med. Imag., vol. 14, pp. 65-80, 1995.
[12] V. S. Khoo, D. P. Dearnaley, D. J. Finnigan, A. Padhani, S. F. Tanner, and M. O. Leach, Magnetic resonance imaging (MRI): considerations and applications in radiotheraphy treatment planning, Radiother. Oncol., vol. 42, pp. 1-15, 1997.
[13] H. W. Muller-Gartner, J. M. Links, J. L. Prince, R. N. Bryan, E. McVeigh, J. P. Leal, C. Davatzikos, and J. J. Frost, Measurement of radiotracer concentration in braingray matter using positron emission tomography: MRI-based correction for partial volume effects, J. Cereb. Blood Flow Metab., vol. 12, pp. 571-583, 1992.
[14] W. E. L. Grimson, G. J. Ettinger, T. Kapur, M. E. Leventon,W. M.Wells, et al., Utilizingsegmented MRI data in image-guided surgery, Int-l J. Patt. Recog. Artificial Intell., vol. 11, pp. 1367-1397, 1997.