Segmentation of Cardiac Images by the Force Field Driven Speed Term

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by the Force Field Driven Speed Term</title> <meta name="description" content="Segmentation of Cardiac Images by the Force Field Driven Speed Term"> <meta name="keywords" content="PET, Cardiac, Heart, Mouse, Geodesic, Geometric, Level Sets, Deformable Models, Edge Detection, Segmentation."> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <meta name="citation_title" content="Segmentation of Cardiac Images by the Force Field Driven Speed Term"> <meta name="citation_author" content="Renato Dedic"> <meta name="citation_author" content="Madjid Allili"> <meta name="citation_author" content="Roger Lecomte"> <meta name="citation_author" content="Adbelhamid Benchakroun"> <meta name="citation_publication_date" content="2008/05/21"> <meta name="citation_journal_title" content="International Journal of Physical and Mathematical Sciences"> <meta name="citation_volume" content="2"> <meta 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href="https://publications.waset.org/search?q=Renato%20Dedic">Renato Dedic</a>, <a href="https://publications.waset.org/search?q=Madjid%20Allili"> Madjid Allili</a>, <a href="https://publications.waset.org/search?q=Roger%20Lecomte"> Roger Lecomte</a>, <a href="https://publications.waset.org/search?q=Adbelhamid%20Benchakroun"> Adbelhamid Benchakroun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <iframe src="https://publications.waset.org/9401.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=PET" title="PET">PET</a>, <a href="https://publications.waset.org/search?q=Cardiac" title=" Cardiac"> Cardiac</a>, <a href="https://publications.waset.org/search?q=Heart" title=" Heart"> Heart</a>, <a href="https://publications.waset.org/search?q=Mouse" title=" Mouse"> Mouse</a>, <a href="https://publications.waset.org/search?q=Geodesic" title=" Geodesic"> Geodesic</a>, <a href="https://publications.waset.org/search?q=Geometric" title=" Geometric"> Geometric</a>, <a href="https://publications.waset.org/search?q=Level%20Sets" title=" Level Sets"> Level Sets</a>, <a href="https://publications.waset.org/search?q=Deformable%20Models" title=" Deformable Models"> Deformable Models</a>, <a href="https://publications.waset.org/search?q=Edge%20Detection" title=" Edge Detection"> Edge Detection</a>, <a href="https://publications.waset.org/search?q=Segmentation." title=" Segmentation."> Segmentation.</a> </p> <p class="card-text"><strong>Digital Object Identifier (DOI):</strong> <a href="https://doi.org/10.5281/zenodo.1072698" target="_blank">doi.org/10.5281/zenodo.1072698</a> </p> <a href="https://publications.waset.org/9401/segmentation-of-cardiac-images-by-the-force-field-driven-speed-term" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/9401/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/9401/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/9401/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/9401/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/9401/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/9401/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/9401/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/9401/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/9401/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/9401/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/9401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1211</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] V. Caselles, R. Kimmel, and G. Sapiro, Geodesic Active Contours, Int-l J. Computer Vision, vol. 22, pp. 61-79, 1997. <br>[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. <br>[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. <br>[4] J. Sethian. Level Sets Methods and Fast Marching Methods. Cambridge University Press, 1999. <br>[5] S. Osher och R. Fedkiw. Level Set Methods and Dynamic Implicit Surfaces. Springer-Verlag, 2002. <br>[6] A. Rosenfeld and A.C. Kak, Digital Picture Processing(New York: Academic Press, 1982). <br>[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. <br>[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. <br>[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. <br>[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. <br>[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. <br>[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. <br>[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. <br>[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. 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Segmentation of Cardiac Images by the Force Field Driven Speed Term