Segmentation of Cardiac Images by the Force Field Driven Speed Term
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.
[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.
[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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:58466", author = "Renato Dedic and Madjid Allili and Roger Lecomte and Adbelhamid Benchakroun", title = "Segmentation of Cardiac Images by the Force Field Driven Speed Term", 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.", volume = "2", number = "5", pages = "323-5", }