Abstract
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.
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Nillesen, M.M., Lopata, R.G., Gerrits, I.H., Kapusta, L., Thijssen, J.M., de Korte, C.L.: Modeling envelope statistics of blood and myocardium for segmentation of echocardiographic images. Ultrasound Med. Biol. 34(4), 674–680 (2008)
Yu, Y., Acton, S.: Speckle reducing anisotropic diffusion. IEEE Trans. Img. Proc. 11(11), 1260–1270 (2002)
Aja-Fernandez, S., Alberola-Lopez, C.: On the estimation of the coefficient of variation for anisotropic diffusion speckle filtering. IEEE Trans. Img. Proc. 15(9), 2694–2701 (2006)
Krissian, K., Westin, C.F., Kikinis, R., Vosburgh, K.: Oriented speckle reducing anisotropic diffusion. IEEE Trans. Img. Proc. 16(5), 1412–1424 (2007)
Tao, Z., Tagare, H.D., Beaty, J.D.: Evaluation of four probability distribution models for speckle in clinical cardiac ultrasound images. IEEE Trans. Med. Imag. 25(11), 1483–1491 (2006)
Eltoft, T.: Modeling the amplitude statistics of ultrasonic images. IEEE Trans. Med. Imag. 25(2), 229–240 (2006)
Wagner, R., Smith, S., Sandrik, J., Lopez, H.: Statistics of speckle in ultrasound B-scans. IEEE Trans. Sonics Ultrason. 30(3), 156–163 (1983)
Figueiredo, M., Jain, A.: Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 381–396 (2002)
Krissian, K., Aja-Fernández, S.: Noise-driven anisotropic diffusion filtering of MRI. IEEE Trans. Img. Proc. 18(10), 2265–2274 (2009)
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Vegas-Sanchez-Ferrero, G., Aja-Fernandez, S., Martin-Fernandez, M., Frangi, A.F., Palencia, C. (2010). Probabilistic-Driven Oriented Speckle Reducing Anisotropic Diffusion with Application to Cardiac Ultrasonic Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15705-9_63
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DOI: https://doi.org/10.1007/978-3-642-15705-9_63
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