Navegar

Colección

Datos Estadísticas

Artículo

Estamos trabajando para incorporar este artículo al repositorio
Consulte el artículo en la página del editor
Consulte la política de Acceso Abierto del editor

Abstract:

Within the family of statistical image segmentation methods, those based on Bayesian inference have been commonly applied to classify brain tissues as obtained with Magnetic Resonance Imaging (MRI). In this framework we present an unsupervised algorithm to account for the main tissue classes that constitute MR brain volumes. Two models are examined: the Discrete Model (DM), in which every voxel belongs to a single tissue class, and the Partial Volume Model (PVM), where two classes may be present in a single voxel with a certain probability. We make use of the Maximum Evidence (ME) criterion to estimate the most probable parameters describing each model in a separate fashion. Since an exact image inference would be computationally very expensive, we propose an approximate algorithm for model optimization. Such method was tested on a simulated MRI-T1 brain phantom in 3D, as well as on clinical MR images. As a result, we found that the PVM slightly outperforms the DM, both in terms of Evidence and Mean Absolute Error (MAE). We also show that the Evidence is a very useful figure of merit for error prediction as well as a convenient tool to determine the most probable model from measured data. © 2009 Elsevier B.V. All rights reserved.

Registro:

Documento: Artículo
Título:Maximum Evidence Method for classification of brain tissues in MRI
Autor:Isoardi, R.A.; Oliva, D.E.; Mato, G.
Filiación:Fundación Escuela de Medicina Nuclear (CNEA and FUESMEN), Mendoza, Argentina
Centro Atómico Bariloche and Instituto Balseiro (CNEA and CONICET), S. C. de Bariloche, Argentina
Laboratorio de Neurobiología de la Memoria, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Buenos Aires, Argentina
Palabras clave:Bayesian estimation; Image segmentation; Magnetic Resonance Imaging; Partial volume effect; Approximate algorithms; Bayesian estimations; Bayesian inference; Brain phantoms; Brain tissue; Brain volume; Discrete models; Error prediction; Figure of merit; Mean absolute error; Measured data; Model optimization; MR images; Partial volume effect; Partial volumes; Single voxel; Statistical image segmentation; Unsupervised algorithms; Bayesian networks; Brain; Histology; Image segmentation; Inference engines; Probability density function; Resonance; Three dimensional; Tissue; Magnetic resonance imaging
Año:2011
Volumen:32
Número:1
Página de inicio:12
Página de fin:18
DOI: http://dx.doi.org/10.1016/j.patrec.2009.09.006
Título revista:Pattern Recognition Letters
Título revista abreviado:Pattern Recogn. Lett.
ISSN:01678655
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01678655_v32_n1_p12_Isoardi

Referencias:

  • Besag, J., On the statistical analysis of dirty pictures (1986) J. Roy. Statist. Soc. Ser. B, 48, pp. 259-302
  • Bezdek, J., Hall, L., Clarke, L., Review of MR image segmentation techniques using pattern recognition (1993) Med. Phys., 20, pp. 1033-1048
  • Barker, S.A., Rayner, P.J.W., Unsupervised image segmentation using Markov random fields models (2000) Pattern Recognition, 33, pp. 587-602
  • Brummer, M., Mersereau, R., Eisner, R., Lewine, R., Automatic detection of brain contours in MRI data sets (1993) IEEE Trans. Med. Imag., 12, pp. 153-166
  • Collins, D., Zijdenbos, A., Kollokian, V., Sled, J., Kabani, N., Holmes, C., Evans, A., Design and construction of a realistic digital brain phantom (1998) IEEE Trans. Med. Imag., 17, pp. 463-468
  • Descombes, X., Morris, R.D., Estimation of Markov random field prior parameters using Markov chain Montecarlo maximum likelihood (1999) IEEE Trans. Imag. Proc., 8, pp. 954-963
  • Frery, A.C., Correia, A.H., Freitas Da, C.C., Classifying multifrequency fully polarimetric imagery with multiple sources of statistical evidence and contextual information (2007) IEEE Trans. Geosci. Remote Sens., 45, pp. 3098-3109
  • Geman, S., Geman, D., Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images (1984) IEEE Trans. PAMI, 6, pp. 721-741
  • Gull, S., (1989) Developments in Maximum Entropy Data Analysis in Maximum Entropy and Bayesian Methods, pp. 3-71
  • Held, K., Rota Kops, E., Krause, B., Iii, W.W., Kikinis, R., Müller- Gärtner, R., Markov random field segmentation of brain images (1997) IEEE Trans. Med. Imag., 16, pp. 878-886
  • Kitamoto, A., Katagi, M., Image classification using probabilistic models that reflect the internal structure of mixels (1999) Pattern Anal. Appl., 2, pp. 32-43
  • Kwan, R., Evans, A., Pike, G., An extensible MRI simulator for post-processing evaluation (1996) Lecture Notes in Computer Science, 1131. , Springer-Verlag
  • Laidlaw, D., Fleischer, K., Barr, A., Partial volume Bayesian classification of material mixtures in MR volume data using voxel histograms (1998) IEEE Trans. Med. Imag., 17, pp. 74-86
  • Levada, A.L.M., Mascarenhas, N.D.A., Tannús, A., Pseudolikelihood equations for Potts MRF model parameter estimation on higher order neighborhood systems (2008) IEEE Geosci. Remote Sens. Lett., 5, pp. 522-526
  • MacKay, D., Bayesian interpolation (1992) Neural Comput., 4, pp. 415-447
  • Marroquin, J.L., Vemuri, B.C., Botello, S., Calderon, F., Fernandez-Bouzas, S., An accurate and efficient Bayesian method for automatic segmentation of brain MRI (2002) IEEE Trans. Med. Imag., 21, pp. 934-945
  • Neal, R., Bayesian Training of Backpropagation Networks by the Hybrid Monte Carlo method (1992) Technical Report CRG-TR-92-1, , Dept. of Computer Science, University of Toronto
  • Oliva, D.E., Isoardi, R.A., Mato, G., Bayesian estimation of hyperparameters in MRI through the maximum evidence method (2008) XXI Brazilian Sympos. on Computer Graphics and Image Processing (SIBGRAPI 08), pp. 129-136. , IEEE Computer Society Press, Campo Grande
  • Ruan, S., Jaggi, C., Xue, J., Fadili, J., Bloyet, D., Brain tissue classification of magnetic resonance images using partial volume modeling (2000) IEEE Trans. Med. Imag., 19, pp. 1179-1187
  • Shattuck, D.W., Leahy, R.M., Brainsuite: An automated cortical surface identification tool (2002) Med. Image Anal., 8, pp. 129-142
  • Tohka, J., Zijbendos, A., Evans, A., Fast and robust parameter estimation for statistical partial volume models in brain MRI (2004) NeuroImage, 23, pp. 84-97
  • Van Leemput, K., Vandermeulen, D., Suetens, P., A unifying framework for partial volume segmentation of brain MR images (2003) IEEE Trans. Med. Imag., 22, pp. 105-119
  • Weickert, J., Ter Haar Romeny, B., Viergever, M., Efficient and reliable schemes for nonlinear diffusion filtering (1998) IEEE Trans. Image Processing, 7, pp. 398-410
  • Wells, W., Kikinis, R., Grimson, W., Jolesz, F., Adaptative segmentation of MRI data (1996) IEEE Trans. Med. Imag., 15, pp. 429-442
  • Zhang, Y., Brady, M., Smith, S., Segmentation of brain images through a hidden Markov random field model and the expectation-maximization algorithm (2001) IEEE Trans. Med. Imag., 20, pp. 45-57

Citas:

---------- APA ----------
Isoardi, R.A., Oliva, D.E. & Mato, G. (2011) . Maximum Evidence Method for classification of brain tissues in MRI. Pattern Recognition Letters, 32(1), 12-18.
http://dx.doi.org/10.1016/j.patrec.2009.09.006
---------- CHICAGO ----------
Isoardi, R.A., Oliva, D.E., Mato, G. "Maximum Evidence Method for classification of brain tissues in MRI" . Pattern Recognition Letters 32, no. 1 (2011) : 12-18.
http://dx.doi.org/10.1016/j.patrec.2009.09.006
---------- MLA ----------
Isoardi, R.A., Oliva, D.E., Mato, G. "Maximum Evidence Method for classification of brain tissues in MRI" . Pattern Recognition Letters, vol. 32, no. 1, 2011, pp. 12-18.
http://dx.doi.org/10.1016/j.patrec.2009.09.006
---------- VANCOUVER ----------
Isoardi, R.A., Oliva, D.E., Mato, G. Maximum Evidence Method for classification of brain tissues in MRI. Pattern Recogn. Lett. 2011;32(1):12-18.
http://dx.doi.org/10.1016/j.patrec.2009.09.006