TY - GEN
T1 - Probabilistic-driven oriented speckle reducing anisotropic diffusion with application to cardiac ultrasonic images
AU - Vegas-Sanchez-Ferrero, G.
AU - Aja-Fernandez, S.
AU - Martin-Fernandez, M.
AU - Frangi, A. F.
AU - Palencia, C.
PY - 2010
Y1 - 2010
N2 - A novel anisotropic diffusion filter is proposed in this work with application to cardiac ultrasonic images. It includes probabilistic models which describe the probability density function (PDF) of tissues and adapts the diffusion tensor to the image iteratively. For this purpose, a preliminary study is performed in order to select the probability models that best fit the stastitical behavior of each tissue class in cardiac ultrasonic images. Then, the parameters of the diffusion tensor are defined taking into account the statistical properties of the image at each voxel. When the structure tensor of the probability of belonging to each tissue is included in the diffusion tensor definition, a better boundaries estimates can be obtained instead of calculating directly the boundaries from the image. This is the main contribution of this work. Additionally, the proposed method follows the statistical properties of the image in each iteration. This is considered as a second contribution since state-of-the-art methods suppose that noise or statistical properties of the image do not change during the filter process.
AB - A novel anisotropic diffusion filter is proposed in this work with application to cardiac ultrasonic images. It includes probabilistic models which describe the probability density function (PDF) of tissues and adapts the diffusion tensor to the image iteratively. For this purpose, a preliminary study is performed in order to select the probability models that best fit the stastitical behavior of each tissue class in cardiac ultrasonic images. Then, the parameters of the diffusion tensor are defined taking into account the statistical properties of the image at each voxel. When the structure tensor of the probability of belonging to each tissue is included in the diffusion tensor definition, a better boundaries estimates can be obtained instead of calculating directly the boundaries from the image. This is the main contribution of this work. Additionally, the proposed method follows the statistical properties of the image in each iteration. This is considered as a second contribution since state-of-the-art methods suppose that noise or statistical properties of the image do not change during the filter process.
KW - Anisotropic diffusion
KW - cardiac ultrasonic images
KW - diffusion tensor
KW - probability models
KW - structure tensor
UR - http://www.scopus.com/inward/record.url?scp=78349284398&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15705-9_63
DO - 10.1007/978-3-642-15705-9_63
M3 - Conference contribution
AN - SCOPUS:78349284398
SN - 3642157041
SN - 9783642157042
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 518
EP - 525
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
Y2 - 20 September 2010 through 24 September 2010
ER -