A Fuzzy Logic Approach to Identifying Brain Structures in MRI Using Expert Anatomic Knowledge

We report a novel computer method for automatic labeling of structures in 3D MRI data sets using expert anatomical knowledge that is coded in fuzzy sets and fuzzy rules. The method first identifies major structures and then uses spatial relationships to these landmarks to recognize other structures. This labeling process simulates the iterative process that we ourselves use to locate structures in images. We demonstrate its application in three data sets, labeling brain MRI by locating the longitudinal and lateral fissures and the central sulci and then determining boundaries for the frontal lobes. Our method is adaptable to the identification of other anatomical structures.

[1]  James M. Keller,et al.  Quantitative analysis of properties and spatial relations of fuzzy image regions , 1993, IEEE Trans. Fuzzy Syst..

[2]  Thomas S. Huang,et al.  Image processing , 1971 .

[3]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[4]  M. Raichle,et al.  A Stereotactic Method of Anatomical Localization for Positron Emission Tomography , 1985, Journal of computer assisted tomography.

[5]  Thomas A. Kent,et al.  A two-stage human brain MRI segmentation scheme using fuzzy logic , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[6]  Joseph A. Kufera,et al.  Frontal lobe changes after severe diffuse closed head injury in children: a volumetric study of magnetic resonance imaging. , 1995, Neurosurgery.

[7]  D. Louis Collins,et al.  Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .

[8]  E. Bullmore,et al.  Activation of auditory cortex during silent lipreading. , 1997, Science.

[9]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[10]  A. Benton,et al.  Frontal Lobe Function and Dysfunction , 1991 .

[11]  J. Talairach,et al.  Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging , 1988 .

[12]  E. Courchesne,et al.  Attentional Activation of the Cerebellum Independent of Motor Involvement , 1997, Science.

[13]  N C Andreasen,et al.  Techniques for measuring sulcal/gyral patterns in the brain as visualized through magnetic resonance scanning: BRAINPLOT and BRAINMAP. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[14]  S P Raya,et al.  Low-level segmentation of 3-D magnetic resonance brain images-a rule-based system. , 1990, IEEE transactions on medical imaging.

[15]  John C. Mazziotta,et al.  Physiologic Neuroanatomy: New Brain Imaging Methods Present a Challenge to an Old Discipline , 1984, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  L O Hall,et al.  Review of MR image segmentation techniques using pattern recognition. , 1993, Medical physics.

[17]  D G Brunder,et al.  Measurement of brain compartment volumes in MR using voxel composition calculations. , 1991, Journal of computer assisted tomography.

[18]  M. F. Huerta,et al.  The human brain project: an international resource , 1993, Trends in Neurosciences.

[19]  J. Ehrhardt,et al.  Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging. , 1994, Science.

[20]  R. Thatcher.  Functional neuroimaging : technical foundations , 1994 .

[21]  小野 道夫,et al.  Atlas of the Cerebral Sulci , 1990 .

[22]  John Yen,et al.  Fuzzy rule-based system for labeling the structures in 3D human brain magnetic , 1996 .

[23]  Lawrence O. Hall,et al.  Knowledge-based classification and tissue labeling of MR images of human brain , 1993, IEEE Trans. Medical Imaging.

[24]  Richard M. Leahy,et al.  Matching deformable atlas models to preprocessed magnetic resonance brain images , 1994, Proceedings of 1st International Conference on Image Processing.