Poster
# 40
Poster |
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6th Internet World Congress for Biomedical Sciences |
Juan Gutierrez Aguado(1), Isaac Llorens i Eixea(2), Ricardo Ferris Castell(3)
(1)(2)Facultat de Fisica. Universitat de Valencia - Burjassot. Spain
(3)Universitat de Valencia - Burjasot. Spain
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[New Technology] |
[Medical Electronics & Engineering] |
[Radiology & Nuclear Medicine] |
Segmentation is commonly used to discriminate objects in a scene. Contour detection is a possible way to segment images. In grey level images, changes in the intensity values (by means of numerical derivation) give a first approximation on where are located the possible object contours, but this does not give a global description of contours, due to illumination changes or noise. As several authors have noted it, numerical derivation is an inverse problem and ill-posed in the sense of Hadammard, that can be regularized using Tikhonov stabilizers.
A snake (1) is a set of points over which energy is defined. In this energy there are two terms, one of them is the derivation and the other one is a Tikhonov stabilizer. The first term controls which feature the snake is attracted towards (in this case, this term is related to edge information). This term can be interpreted as an external force acting over each point. The second term controls the elasticity and the rigidity of snakes and depends on the relative position of points. This second term can be interpreted as an internal force. The next step is to obtain the minimal energy. Energy minimizing methods are: numerical resolution (1), dynamic programming (2), Hopfield neural nets, greedy algorithms (3) and simulated annealing.
In this paper, it is presented a B-snake, defined as a snake where points are represented by means of a cubic B-Spline (4), and the energy is defined throughout the whole curve. A greedy algorithm is chosen to minimize energy.
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[New Technology] |
[Medical Electronics & Engineering] |
[Radiology & Nuclear Medicine] |
