The development of aid's systems for the medical
diagnosis is not easy thing because of presence of inhomogeneities in
the MRI, the variability of the data from a sequence to the other as
well as of other different source distortions that accentuate this
difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is
described in this article. A first phase consists in estimating the
density of probability of the data by the Parzen-Rozenblatt method.
The classification procedure is completely automatic and doesn't
make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an
automatic manner by an operator of mathematical morphology called
skeleton by influence zones detection (SKIZ). The problem of
initialization of the prototypes as well as their number is transformed
in an optimization problem; in more the procedure is adaptive since it
takes in consideration the contextual information presents in every
voxel by an adaptive and robust non parametric model by the
Markov fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior
Marginal).
[1] Bardinet E., Cohen L., and Ayache N., "A parametric deformable model
to fit unstructured 3D data", Technical Report 2617, INRIA Sophia
Antipolis, 1995.
[2] Barillot C., and al., "Représentation mixte numérique/symbolique des
sillons corticaux", In Proc. of 11e congrès Reconnaissance des Formes
et Intelligence Artificielle, Paris, France, pp. 165-174,1998.
[3] Bezdek J.C. and Pal N.R., "Some new indexes of cluster validity". IEEE
Trans. Systems Man. Cybern., vol. 28, pp. 301-315, 1998.
[4] Chiou G. and Hwang J.N., "A Neural Network-Based Stochastic Active
Contour Model (NNS-SNAKE) for Contour Finding of Distinct
Features", IEEE Transactions on Image Processing, vol. 4, no. 10, pp.
1407-1416, 1995.
[5] Cootes T.F., and al., "Active Shape Models- Their Training and
Application", Computer Vision and Image Understanding, vol. 61, no.1
1, pp. 38-59, January 1995.
[6] Davatzikos C. and Prince J.L., "An active contour model for mapping
the cortex", IEEE Transactions on Medical Imaging, vol. 14, no. 1, pp.
65-80, 1995.
[7] Dellepiane S., Venturi G., and Vernazza G., "A fuzzy model for the
processing and recognition of MR pathological images", In Lecture
notes in computer science, IPMI proceedings, pp. 444-457, 1991.
[8] Descombes X., Moris R. and Zerubia J., "Quelques améliorations ├á la
segmentation d'images bayesienne", Traitement du signal, vol. 14, no. 4,
pp. 373-382, 1997.
[9] Descombes X., Sigelle M., and Préteux F., "Estimating Gaussian
Markov random field parameters in a non-stationary framework", IEEE
Transaction On Image Processing, 1998.
[10] Duta N. and Sonka M., "Segmentation and Interpretation of MR Brain
Images : An Improved Active Shape Model", EEE Transactions on
Medical Imaging, vol. 17, no. 6, pp. 1049-1062, 1998.
[11] Friston K. and al., "Statistical Parametric Maps in Functional Imaging : a
general linear approach", Human Brain Mapping, vol. 2, pp. 189-210,
1995.
[12] Geman D., Renolds G., "Constrained restoration and the recovery of
discontinuities", IEEE Transaction On Pattern Analysis and Machine
Intelligence, vol. 14, no. 3, pp. 367-383, Marsh 1992.
[13] Géraud T., "Segmentation des structures internes du cerveau en
imagerie par résonance magnétique 3D", Thèse de doctorat, Télécom
Paris, 1998.
[14] Gong L. and Kulikowski A., "Composition of image Analysis Processes
Through Object-centered Hierarchical Planning", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. pp. 997-
1009, 1995.
[15] Held K., and al., "Markov Random Field Segmentation of Brain MR
Images", IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp.
878-886, 1997.
[16] Herbin M., Bonnet N. and Vautrot P., "Estimation of the number of
clusters by influence zones", Pattern Recognition Letters, vol. 22, pp.
1557-1568, 2001.
[17] Jaggi C., Segmentation par méthode markovienne de l-encéphale
humain par résonance magnétique : théorie, mise en oeuvre et
évaluation, Thèse de doctorat de l-université de Caen, 1998.
[18] Kapur T. and al., "Segmentation of brain tissue from magnetic resonance
images", Medical Image Analysis, vol.1, no. 2, pp. 109-127, 1996.
[19] Li H., and al., "Object-recognition in brain CTscans : knowledge-based
fusion of data from multiple feature extractors", IEEE Transactions on
Medical Imaging, vol. 14, no. 2, pp. 212-229, 1995.
[20] Liew A.W.-C., Hong Yan, "Adaptive fuzzy segmentation of 3D MR
brain images", Fuzzy Systems, The 12th IEEE International Conference
on Fuzzy System, vol. 2 , pp. 25-28, May 2003
[21] Mangin J.F. and al., "A MRF based random graph modeling the human
cortical topography", In First International Conference CVRMed, Nice,
pp. 177-183, 1995.
[22] Moussaoui A. and Chen V., "Fuzzy automatic classification without a
prior knowledge-mean shift application to MRN brain images
segmentation", In Symposium of Electronics and Telecommunications,
Timisoara, Romania, pp. 213-219, October 2004.
[23] Moussaoui A. and Chen V., "Segmentation d'images médicales par
coopération de classifieurs", In Proc. of International Symposium on
Medical Image Processing, Blida, Algeria, pp. 57-62, November 2004.
[24] Moussaoui A. and Chen V., "Segmentation neuro-floue des tissus
cérébraux pathologiques en IRM 3D", In Proc. of International
Symposium on Medical Image Processing, Blida, Algeria, pp. 81-86,
November 2004.
[25] Palubinskas G., Miter S. and Poggio T., "An unsupervised clustering
method using the entropy minimisation". In ICPR. - Austria, August
1998.
[26] Parzen E., "On estimation of a probability density function and mode".
Ann. Math. Statist., vol. 33, pp. 1065-1076, 1962.
[27] Rajapakse J., Giedd J., Rapoport and J., "Statistical Approach to
segmentation of single-channel cerebral MR images", IEEE
Transactions on Medical Imaging, vol. 16, pp. 176-186, 1997.
[28] Sandor S. and Leahy R., "Surface-Based Labeling of Cortical Anatomy
Using a Deformable Atlas", IEEE Transactions on Medical Imaging,
vol. 16, no. 1, pp. 41-54, 1997.
[29] Sigele M. and Ronfard R., "Modèles de potts et relaxation d'images de
labels par champs de Markov", Traitement du signal, vol. 6, no. 9, pp.
449-458, 1992.
[30] Silverman B. W., "Density Estimation for Statistics and Data Analysis",
Chapman and Hall, London, 1986.
[31] Soltanian-Zadeh H., and al, "Model-independent method for fMRI
analysis ", EEE Transactions on Medical Imaging, vol. 23, no.3, pp. 285
- 296, March 2004.
[32] Sonka M., Tadikonda S., and Collins A. M., "Knowledge-Based
Interpretation of MR brain Images", IEEE transactions on Medical
Imaging, vol. 15, no. 4, 1996.
[33] Staib L.H. and Duncan J.S., "Boundary Finding with Parametrically
Deformable Models", IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 14, no. 11, pp. 1061-1075, 1992.
[34] Vaillant M. and C.Davatzikos, "Finding parametric representations of
the cortical sulci using an active contour model", Medical Image
Analysis, vol. 1, no. 4, pp. 295-315, 1997.
[35] Wells W.M., and al., "Adaptative Segmentation of MRI data", IEEE
Transactions on Medical Imaging, vol. 15, no. 4, pp. 429-442, 1996.
[1] Bardinet E., Cohen L., and Ayache N., "A parametric deformable model
to fit unstructured 3D data", Technical Report 2617, INRIA Sophia
Antipolis, 1995.
[2] Barillot C., and al., "Représentation mixte numérique/symbolique des
sillons corticaux", In Proc. of 11e congrès Reconnaissance des Formes
et Intelligence Artificielle, Paris, France, pp. 165-174,1998.
[3] Bezdek J.C. and Pal N.R., "Some new indexes of cluster validity". IEEE
Trans. Systems Man. Cybern., vol. 28, pp. 301-315, 1998.
[4] Chiou G. and Hwang J.N., "A Neural Network-Based Stochastic Active
Contour Model (NNS-SNAKE) for Contour Finding of Distinct
Features", IEEE Transactions on Image Processing, vol. 4, no. 10, pp.
1407-1416, 1995.
[5] Cootes T.F., and al., "Active Shape Models- Their Training and
Application", Computer Vision and Image Understanding, vol. 61, no.1
1, pp. 38-59, January 1995.
[6] Davatzikos C. and Prince J.L., "An active contour model for mapping
the cortex", IEEE Transactions on Medical Imaging, vol. 14, no. 1, pp.
65-80, 1995.
[7] Dellepiane S., Venturi G., and Vernazza G., "A fuzzy model for the
processing and recognition of MR pathological images", In Lecture
notes in computer science, IPMI proceedings, pp. 444-457, 1991.
[8] Descombes X., Moris R. and Zerubia J., "Quelques améliorations ├á la
segmentation d'images bayesienne", Traitement du signal, vol. 14, no. 4,
pp. 373-382, 1997.
[9] Descombes X., Sigelle M., and Préteux F., "Estimating Gaussian
Markov random field parameters in a non-stationary framework", IEEE
Transaction On Image Processing, 1998.
[10] Duta N. and Sonka M., "Segmentation and Interpretation of MR Brain
Images : An Improved Active Shape Model", EEE Transactions on
Medical Imaging, vol. 17, no. 6, pp. 1049-1062, 1998.
[11] Friston K. and al., "Statistical Parametric Maps in Functional Imaging : a
general linear approach", Human Brain Mapping, vol. 2, pp. 189-210,
1995.
[12] Geman D., Renolds G., "Constrained restoration and the recovery of
discontinuities", IEEE Transaction On Pattern Analysis and Machine
Intelligence, vol. 14, no. 3, pp. 367-383, Marsh 1992.
[13] Géraud T., "Segmentation des structures internes du cerveau en
imagerie par résonance magnétique 3D", Thèse de doctorat, Télécom
Paris, 1998.
[14] Gong L. and Kulikowski A., "Composition of image Analysis Processes
Through Object-centered Hierarchical Planning", IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 17, no. 10, pp. pp. 997-
1009, 1995.
[15] Held K., and al., "Markov Random Field Segmentation of Brain MR
Images", IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp.
878-886, 1997.
[16] Herbin M., Bonnet N. and Vautrot P., "Estimation of the number of
clusters by influence zones", Pattern Recognition Letters, vol. 22, pp.
1557-1568, 2001.
[17] Jaggi C., Segmentation par méthode markovienne de l-encéphale
humain par résonance magnétique : théorie, mise en oeuvre et
évaluation, Thèse de doctorat de l-université de Caen, 1998.
[18] Kapur T. and al., "Segmentation of brain tissue from magnetic resonance
images", Medical Image Analysis, vol.1, no. 2, pp. 109-127, 1996.
[19] Li H., and al., "Object-recognition in brain CTscans : knowledge-based
fusion of data from multiple feature extractors", IEEE Transactions on
Medical Imaging, vol. 14, no. 2, pp. 212-229, 1995.
[20] Liew A.W.-C., Hong Yan, "Adaptive fuzzy segmentation of 3D MR
brain images", Fuzzy Systems, The 12th IEEE International Conference
on Fuzzy System, vol. 2 , pp. 25-28, May 2003
[21] Mangin J.F. and al., "A MRF based random graph modeling the human
cortical topography", In First International Conference CVRMed, Nice,
pp. 177-183, 1995.
[22] Moussaoui A. and Chen V., "Fuzzy automatic classification without a
prior knowledge-mean shift application to MRN brain images
segmentation", In Symposium of Electronics and Telecommunications,
Timisoara, Romania, pp. 213-219, October 2004.
[23] Moussaoui A. and Chen V., "Segmentation d'images médicales par
coopération de classifieurs", In Proc. of International Symposium on
Medical Image Processing, Blida, Algeria, pp. 57-62, November 2004.
[24] Moussaoui A. and Chen V., "Segmentation neuro-floue des tissus
cérébraux pathologiques en IRM 3D", In Proc. of International
Symposium on Medical Image Processing, Blida, Algeria, pp. 81-86,
November 2004.
[25] Palubinskas G., Miter S. and Poggio T., "An unsupervised clustering
method using the entropy minimisation". In ICPR. - Austria, August
1998.
[26] Parzen E., "On estimation of a probability density function and mode".
Ann. Math. Statist., vol. 33, pp. 1065-1076, 1962.
[27] Rajapakse J., Giedd J., Rapoport and J., "Statistical Approach to
segmentation of single-channel cerebral MR images", IEEE
Transactions on Medical Imaging, vol. 16, pp. 176-186, 1997.
[28] Sandor S. and Leahy R., "Surface-Based Labeling of Cortical Anatomy
Using a Deformable Atlas", IEEE Transactions on Medical Imaging,
vol. 16, no. 1, pp. 41-54, 1997.
[29] Sigele M. and Ronfard R., "Modèles de potts et relaxation d'images de
labels par champs de Markov", Traitement du signal, vol. 6, no. 9, pp.
449-458, 1992.
[30] Silverman B. W., "Density Estimation for Statistics and Data Analysis",
Chapman and Hall, London, 1986.
[31] Soltanian-Zadeh H., and al, "Model-independent method for fMRI
analysis ", EEE Transactions on Medical Imaging, vol. 23, no.3, pp. 285
- 296, March 2004.
[32] Sonka M., Tadikonda S., and Collins A. M., "Knowledge-Based
Interpretation of MR brain Images", IEEE transactions on Medical
Imaging, vol. 15, no. 4, 1996.
[33] Staib L.H. and Duncan J.S., "Boundary Finding with Parametrically
Deformable Models", IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 14, no. 11, pp. 1061-1075, 1992.
[34] Vaillant M. and C.Davatzikos, "Finding parametric representations of
the cortical sulci using an active contour model", Medical Image
Analysis, vol. 1, no. 4, pp. 295-315, 1997.
[35] Wells W.M., and al., "Adaptative Segmentation of MRI data", IEEE
Transactions on Medical Imaging, vol. 15, no. 4, pp. 429-442, 1996.
@article{"International Journal of Medical, Medicine and Health Sciences:54998", author = "Abdelouahab Moussaoui and Nabila Ferahta and Victor Chen", title = "A New Hybrid RMN Image Segmentation Algorithm", abstract = "The development of aid's systems for the medical
diagnosis is not easy thing because of presence of inhomogeneities in
the MRI, the variability of the data from a sequence to the other as
well as of other different source distortions that accentuate this
difficulty. A new automatic, contextual, adaptive and robust
segmentation procedure by MRI brain tissue classification is
described in this article. A first phase consists in estimating the
density of probability of the data by the Parzen-Rozenblatt method.
The classification procedure is completely automatic and doesn't
make any assumptions nor on the clusters number nor on the
prototypes of these clusters since these last are detected in an
automatic manner by an operator of mathematical morphology called
skeleton by influence zones detection (SKIZ). The problem of
initialization of the prototypes as well as their number is transformed
in an optimization problem; in more the procedure is adaptive since it
takes in consideration the contextual information presents in every
voxel by an adaptive and robust non parametric model by the
Markov fields (MF). The number of bad classifications is reduced by
the use of the criteria of MPM minimization (Maximum Posterior
Marginal).", keywords = "Clustering, Automatic Classification, SKIZ, MarkovFields, Image segmentation, Maximum Posterior Marginal (MPM).", volume = "1", number = "12", pages = "616-8", }
