publication . Article . Other literature type . 2011

A Supervised Patch-Based Approach for Human Brain Labeling

François Rousseau;
Open Access
  • Published: 12 Oct 2011 Journal: IEEE Transactions on Medical Imaging, volume 30, pages 1,852-1,862 (issn: 0278-0062, eissn: 1558-254X, Copyright policy)
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • Country: France
Abstract
International audience; We propose in this work a patch-based image labeling method relying on a label propagation frame- work. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any non-rigid registration is presented. Following recent devel- opments in non-local image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in-vivo MR images show that the proposed method is very successful in providing automated human brain labeling.
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Electrical and Electronic Engineering, Radiological and Ultrasound Technology, Software, Computer Science Applications, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Article
Funded by
NIH| High Resolution In-Utero Mapping of Fetal Brain Development from Combined MRI
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01NS055064-01A1
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
,
EC| FBRAIN
Project
FBRAIN
Computational Anatomy of Fetal Brain
  • Funder: European Commission (EC)
  • Project Code: 207667
  • Funding stream: FP7 | SP2 | ERC

[1] A. Akselrod-Ballin, M. Galun, J. M. Gomori, A. Brandt, and R. Basri. Prior knowledge driven multiscale segmentation of brain MRI. Medical Image Computing and Computer-Assisted Intervention: MICCAI , 10(Pt 2):118-126, 2007.

[7] D. L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping, 3(3):190-208, 1995.

[9] P. Coup´e, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transactions on Medical Imaging, 27(4):425-441, April 2008. [OpenAIRE]

[10] P. Coup´e, J. V. Manj´on, V. Fonov, J. Pruessner, M. Robles, and D. L. Collins. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2):940-954, January 2011.

[11] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley-Interscience, 2 edition, October 2000.

[21] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226-239, 1998. [OpenAIRE]

[22] A. Klein, J. Andersson, B. A. Ardekani, J. Ashburner, B. Avants, M. -C. Chiang, G. E. Christensen, D. L. Collins, J. Gee, P. Hellier, J. H. Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren, R. P. Woods, J. J. Mann, and R. V. Parsey. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3):786-802, July 2009. [OpenAIRE]

[23] A. Klein and J. Hirsch. Mindboggle: a scatterbrained approach to automate brain labeling. NeuroImage, 24(2):261-280, January 2005.

[24] J.L. Lancaster, L.H. Rainey, J.L. Summerlin, C.S. Freitas, P.T. Fox, A.C. Evans, A.W. Toga, and J.C. Mazziotta. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human Brain Mapping, 5(4):238-242, 1997.

[25] T. Langerak, U. van der Heide, A. Kotte, M. Viergever, M. van Vulpen, and J. Pluim. Label fusion in Atlas-Based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Transactions on Medical Imaging, July 2010. [OpenAIRE]

[26] J. M. Lo¨tj¨onen, R. Wolz, J. R. Koikkalainen, L. Thurfjell, G. Waldemar, H. Soininen, and D. Rueckert. Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage, 49(3):2352- 2365, February 2010.

[27] M. Mignotte. A non-local regularization strategy for image deconvolution. Pattern Recogn. Lett., 29(16):2206-2212, 2008. [OpenAIRE]

[28] M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander. Mathematical textbook of deformable neuroanatomies. Proceedings of the National Academy of Sciences of the United States of America, 90(24):11944-11948, December 1993. [OpenAIRE]

Abstract
International audience; We propose in this work a patch-based image labeling method relying on a label propagation frame- work. Based on image intensity similarities between the input image and an anatomy textbook, an original strategy which does not require any non-rigid registration is presented. Following recent devel- opments in non-local image denoising, the similarity between images is represented by a weighted graph computed from an intensity-based distance between patches. Experiments on simulated and in-vivo MR images show that the proposed method is very successful in providing automated human brain labeling.
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Electrical and Electronic Engineering, Radiological and Ultrasound Technology, Software, Computer Science Applications, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Article
Funded by
NIH| High Resolution In-Utero Mapping of Fetal Brain Development from Combined MRI
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1R01NS055064-01A1
  • Funding stream: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
,
EC| FBRAIN
Project
FBRAIN
Computational Anatomy of Fetal Brain
  • Funder: European Commission (EC)
  • Project Code: 207667
  • Funding stream: FP7 | SP2 | ERC

[1] A. Akselrod-Ballin, M. Galun, J. M. Gomori, A. Brandt, and R. Basri. Prior knowledge driven multiscale segmentation of brain MRI. Medical Image Computing and Computer-Assisted Intervention: MICCAI , 10(Pt 2):118-126, 2007.

[7] D. L. Collins, C. J. Holmes, T. M. Peters, and A. C. Evans. Automatic 3-D model-based neuroanatomical segmentation. Human Brain Mapping, 3(3):190-208, 1995.

[9] P. Coup´e, P. Yger, S. Prima, P. Hellier, C. Kervrann, and C. Barillot. An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Transactions on Medical Imaging, 27(4):425-441, April 2008. [OpenAIRE]

[10] P. Coup´e, J. V. Manj´on, V. Fonov, J. Pruessner, M. Robles, and D. L. Collins. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage, 54(2):940-954, January 2011.

[11] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley-Interscience, 2 edition, October 2000.

[21] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226-239, 1998. [OpenAIRE]

[22] A. Klein, J. Andersson, B. A. Ardekani, J. Ashburner, B. Avants, M. -C. Chiang, G. E. Christensen, D. L. Collins, J. Gee, P. Hellier, J. H. Song, M. Jenkinson, C. Lepage, D. Rueckert, P. Thompson, T. Vercauteren, R. P. Woods, J. J. Mann, and R. V. Parsey. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3):786-802, July 2009. [OpenAIRE]

[23] A. Klein and J. Hirsch. Mindboggle: a scatterbrained approach to automate brain labeling. NeuroImage, 24(2):261-280, January 2005.

[24] J.L. Lancaster, L.H. Rainey, J.L. Summerlin, C.S. Freitas, P.T. Fox, A.C. Evans, A.W. Toga, and J.C. Mazziotta. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Human Brain Mapping, 5(4):238-242, 1997.

[25] T. Langerak, U. van der Heide, A. Kotte, M. Viergever, M. van Vulpen, and J. Pluim. Label fusion in Atlas-Based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Transactions on Medical Imaging, July 2010. [OpenAIRE]

[26] J. M. Lo¨tj¨onen, R. Wolz, J. R. Koikkalainen, L. Thurfjell, G. Waldemar, H. Soininen, and D. Rueckert. Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage, 49(3):2352- 2365, February 2010.

[27] M. Mignotte. A non-local regularization strategy for image deconvolution. Pattern Recogn. Lett., 29(16):2206-2212, 2008. [OpenAIRE]

[28] M. I. Miller, G. E. Christensen, Y. Amit, and U. Grenander. Mathematical textbook of deformable neuroanatomies. Proceedings of the National Academy of Sciences of the United States of America, 90(24):11944-11948, December 1993. [OpenAIRE]

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