Segmentation of the striatum using data fusion

Proposes a new segmentation scheme to detect cerebral structures in MRI acquisitions using numerical information contained in the image and expert knowledge brought by a specialist. This process is divided in three steps: first, information contained in the MR image is extracted using a fuzzy clustering algorithm, and theoretical information concerning the structure to segment is modeled using possibility theory. Information fusion is then processed, followed by a decision step ending the structure segmentation. Heads of caudate nuclei and putamens are segmented using this method. Results are promising and validation is performed using both numerical indexes and assessment by an expert. This method can be applied to any cerebral structure in an MR image, provided that it can be described in terms of shape, direction and distance by an expert and that the contrast and resolution of the MRI are sufficient.

[1]  Arie Tzvieli Possibility theory: An approach to computerized processing of uncertainty , 1990, J. Am. Soc. Inf. Sci..

[2]  W. A. Hanson,et al.  Interactive 3D segmentation of MRI and CT volumes using morphological operations. , 1992, Journal of computer assisted tomography.

[3]  B Gibaud,et al.  Data fusion in medical imaging: merging multimodal and multipatient images, identification of structures and 3D display aspects. , 1993, European journal of radiology.

[4]  Benoit M. Dawant,et al.  Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study , 1993, IEEE Trans. Medical Imaging.

[5]  Isabelle Bloch,et al.  Fuzzy mathematical morphologies: A comparative study , 1995, Pattern Recognit..

[6]  K J Parker,et al.  Segmentation and Feature Extraction Techniques, with Applications to MRI Head Studies , 1995, Magnetic resonance in medicine.

[7]  Hui Zhu,et al.  A deformable region model for locating the boundary of brain tumor , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[8]  S. Vinitski,et al.  3D segmentation in MRI of brain tumors: preliminary results , 1995, Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society.

[9]  J J Mallet,et al.  Delineation and quantitation of brain lesions by fuzzy clustering in positron emission tomography. , 1996, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[10]  A. Sarwal,et al.  A system for MR brain image segmentation , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Isabelle Bloch Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[12]  Dong Pyo Jang,et al.  Contour detection of hippocampus using dynamic contour model and region growing , 1997, Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136).

[13]  A Colin,et al.  A novel tool for rapid prototyping and development of simple 3D medical image processing applications on PCs. , 1997, Computer methods and programs in biomedicine.

[14]  Faith M. Gunning-Dixon,et al.  Differential aging of the human striatum: a prospective MR imaging study. , 1998, AJNR. American journal of neuroradiology.

[15]  M. Skalej,et al.  Magnetic resonance imaging–based volumetry differentiates idiopathic Parkinson's syndrome from multiple system atrophy and progressive supranuclear palsy , 1999, Annals of neurology.

[16]  L. Zadeh Fuzzy sets as a basis for a theory of possibility , 1999 .

[17]  J. Brandt,et al.  Reduced basal ganglia blood flow and volume in pre-symptomatic, gene-tested persons at-risk for Huntington's disease. , 1999, Brain : a journal of neurology.

[18]  J Y Boire,et al.  MRI-SPECT fusion for the synthesis of high resolution 3D functional brain images: a preliminary study. , 1999, Computer methods and programs in biomedicine.

[19]  J Y Boire,et al.  Tissue segmentation on MR images of the brain by possibilistic clustering on a 3D wavelet representation , 2000, Journal of magnetic resonance imaging : JMRI.

[20]  Vincent Barra,et al.  Automatic segmentation of subcortical brain structures in MR images using information fusion , 2001, IEEE Transactions on Medical Imaging.

[21]  Madjid Fathi,et al.  ErratumErratum to “An approach to use linguistic and model-based fuzzy expert knowledge for the analysis of MRT images”: [Image and Vision Computing 19(4):195–206] , 2001 .

[22]  Madjid Fathi,et al.  An approach to use linguistic and model-based fuzzy expert knowledge for the analysis of MRT images , 2001, Image Vis. Comput..