Automated Segmentation of Brain Exterior in MR Images Driven by Empirical Procedures and Anatomical Knowledge

This work demonstrates encouraging initial results for increasing the automation of a practical and precise MR brain image segmentation method. The intensity threshold for segmenting the brain exterior is automatically determined by locating the choroid plexus. This is done by finding peaks in a series of histograms taken over regions specified using anatomical knowledge. Intensity inhomogeneities are accounted for by adjusting the global intensity to match the white matter peak intensity in local regions. The results from 20 different brain scans (over 1000 images) obtained under different conditions are presented to validate the method which was able to determine the appropriate threshold in approximately 80% of the data.

[1]  P. Scheltens,et al.  Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[2]  S. Folstein,et al.  Putamen volume reduction on magnetic resonance imaging exceeds caudate changes in mild Huntington's disease , 1992, Annals of neurology.

[3]  J H Simon,et al.  Quantitative determination of MS-induced corpus callosum atrophy in vivo using MR imaging. , 1987, AJNR. American journal of neuroradiology.

[4]  C R Jack,et al.  Temporal lobe seizures: lateralization with MR volume measurements of the hippocampal formation. , 1990, Radiology.

[5]  David N. Kennedy,et al.  Fourier Shape Analysis Of Anatomic Structures , 1990 .

[6]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[7]  B M Dawant,et al.  Brain segmentation and white matter lesion detection in MR images. , 1994, Critical reviews in biomedical engineering.

[8]  W. Eric L. Grimson,et al.  Adaptive Segmentation of MRI Data , 1995, CVRMed.

[9]  M. LeMay,et al.  Abnormalities of the left temporal lobe and thought disorder in schizophrenia. A quantitative magnetic resonance imaging study. , 1992, The New England journal of medicine.

[10]  D. Kennedy,et al.  Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging. , 1989, IEEE transactions on medical imaging.

[11]  C R Meyer,et al.  Robust three-dimensional object definition in CT and MRI. , 1996, Medical physics.

[12]  D. Kennedy,et al.  The young adult human brain: an MRI-based morphometric analysis. , 1994, Cerebral cortex.

[13]  W. Jagust,et al.  Quantitative NMR measurements of hippocampal atrophy in Alzheimer's disease , 1988, Magnetic resonance in medicine.

[14]  G. Hynd,et al.  Brain morphology in developmental dyslexia and attention deficit disorder/hyperactivity. , 1990, Archives of neurology.

[15]  M S Buchsbaum,et al.  PET and MRI of the thalamus in never-medicated patients with schizophrenia. , 1996, The American journal of psychiatry.

[16]  P. McKenna,et al.  Schizophrenia – a Brain Disease? a Critical Review of Structural and Functional Cerebral Abnormality in the Disorder , 1995, British Journal of Psychiatry.

[17]  A. Galaburda,et al.  Human Cerebral Cortex: Localization, Parcellation, and Morphometry with Magnetic Resonance Imaging , 1992, Journal of Cognitive Neuroscience.

[18]  Jagath C. Rajapakse,et al.  Quantitative Magnetic Resonance Imaging of Human Brain Development: Ages 4–18 , 1996 .

[19]  John C. Russ,et al.  The image processing handbook (3. ed.) , 1995 .

[20]  D. Kennedy,et al.  Magnetic resonance technology in human brain science: Blueprint for a program based upon morphometry , 1989, Brain and Development.

[21]  H. Lubs,et al.  Neuroanatomic differences between dyslexic and normal readers on magnetic resonance imaging scans. , 1991, Archives of neurology.

[22]  T Rogers.  Dyslexia: a survivor's story. , 1991, Journal of learning disabilities.

[23]  V S Caviness,et al.  Quantitative magnetic resonance imaging and studies of degenerative diseases of the developing human brain. , 1992, Brain & development.

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

[25]  P. Renshaw,et al.  Volumetric MRI analysis comparing subjects having attention-deficit hyperactivity disorder with normal controls , 1997, Neurology.

[26]  J. Rapoport,et al.  Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder. , 1996, Archives of general psychiatry.

[27]  G D Pearlson,et al.  Reversal of asymmetry of the planum temporale in schizophrenia. , 1995, The American journal of psychiatry.

[28]  Milan Sonka,et al.  Knowledge-based interpretation of MR brain images , 1996, IEEE Trans. Medical Imaging.

[29]  B. J. Casey,et al.  Quantitative magnetic resonance imaging of human brain development: ages 4-18. , 1996, Cerebral cortex.

[30]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[31]  Arvid Lundervold,et al.  Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images , 1995, IEEE Trans. Medical Imaging.

[32]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

[33]  V S Caviness,et al.  Volumetric analyses of central nervous system neoplasm based on MRI. , 1991, Pediatric neurology.

[34]  Russell M. Mersereau,et al.  Automatic Detection of Brain Contours in MRI Data Sets , 1991, IPMI.

[35]  B. S. Manjunath,et al.  Automated segmentation of brain MR images , 1995, Pattern Recognit..

[36]  David N. Kennedy,et al.  Segmentation of magnetic resonance brain images using analogue constraint satisfaction neural networks , 1994, Image Vis. Comput..

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

[38]  James C. Bezdek,et al.  A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain , 1992, IEEE Trans. Neural Networks.

[39]  Jagath C. Rajapakse,et al.  Statistical approach to segmentation of single-channel cerebral MR images , 1997, IEEE Transactions on Medical Imaging.

[40]  David N. Kennedy,et al.  Precise segmentation of the lateral ventricles and caudate nucleus in MR brain images using anatomically driven histograms , 1998, IEEE Transactions on Medical Imaging.

[41]  M Ashtari,et al.  Computerized volume measurement of brain structure. , 1990, Investigative radiology.