Applications of computer graphics and image processing to 2D and 3D modeling of the functional architecture of visual cortex

Schwartz, Eric L.; Merker, Bjørn; Wolfson, E.; Shaw, Alan C.

doi:10.1109/38.7745

Cited by 32 publications

(11 citation statements)

References 20 publications

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“…1975;LeVay etal. 1985;Schwartz et al 1988). A superficial similarity to the thresholded, anisotropic-filtered noise in Fig.…”

Section: Analysis and Modeling Of The Ocular Dominance Column Patternmentioning

confidence: 81%

“…Middle row: Monkey ocular dominance system, area 17 (striate cortrex). Experimental data reprinted from (Schwartz et al 1988). Data was obtained like pattern of gradient direction, and a trough-like pattern in the filtered noise yields a region of uniform gradient direction.…”

Section: Models For Orientation Columnsmentioning

confidence: 99%

“…More recent anatomical techniques, such as cytochrome oxidase and 2-deoxyglucose, have also provided views of this and other cortical column systems. In previous work (Schwartz et al 1988), we demonstrated the application of computer-aided anatomy to permit accurate reconstruction, from serial sections, of the macaque ocular dominance column pattern as revealed by cytochrome oxidase staining (Fig. 1).…”

Section: Introductionmentioning

confidence: 98%

“…1. Pattern of ocular dominance in macaque determined by computer reconstruction of the brain from serial sections (Schwartz et al 1988) noise as a basis for the synthesis of cortical column patterns.…”

Section: Introductionmentioning

confidence: 99%

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Cat and monkey cortical columnar patterns modeled by bandpass-filtered 2D white noise

Rojer

Schwartz

1990

Biol. Cybern.

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A simple algorithm based on bandpass-filtering of white noise images provides good quality computer reconstruction of the cat and monkey ocular dominance and orientation column patterns. A small number of parameters control the frequency, orientation, "branchedness", and "regularity" of the column patterns. An oriented (anisotropic) bandpass filter followed by a threshold operation models the macaque ocular dominance column pattern and cat orientation column system. An unoriented (isotropic) bandpass filter models the cat ocular dominance column pattern and the macaque orientation column system. The resemblance of computer graphic simulations produced by this algorithm and histological pattern data, is strong. Since this algorithm is very fast, we have been able to extensively explore its parameters space in order to determine filter parameters which closely match the structure of the various cortical systems. In particular, we have applied spectral analysis to our recent computer reconstruction of the macaque ocular dominance column system, and the model produced by the present algorithm is in close agreement with this detailed data analysis.

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“…1975;LeVay etal. 1985;Schwartz et al 1988). A superficial similarity to the thresholded, anisotropic-filtered noise in Fig.…”

Section: Analysis and Modeling Of The Ocular Dominance Column Patternmentioning

confidence: 81%

Section: Models For Orientation Columnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Cat and monkey cortical columnar patterns modeled by bandpass-filtered 2D white noise

Rojer

Schwartz

1990

Biol. Cybern.

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“…Researchers interested primarily in the shape and organization of the cerebral cortex have concentrated instead on representation of the cortex as a surface. Procedures for unfolding of the cortical surface are described in [11]- [13]. These methods concentrate primarily on providing mappings of the cortical surface and do not directly address the automatic atlas matching problem described here.…”

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confidence: 99%

Surface-based labeling of cortical anatomy using a deformable atlas

Sandor

Leahy

1997

IEEE Trans. Med. Imaging

246

148

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Abstract-We describe a computerized method to automatically find and label the cortical surface in three-dimensional (3-D) magnetic resonance (MR) brain images. The approach we take is to model a prelabeled brain atlas as a physical object and give it elastic properties, allowing it to warp itself onto regions in a preprocessed image. Preprocessing consists of boundary-finding and a morphological procedure which automatically extracts the brain and sulci from an MR image and provides a smoothed representation of the brain surface to which the deformable model can rapidly converge. Our deformable models are energyminimizing elastic surfaces that can accurately locate image features. The models are parameterized with 3-D bicubic B-spline surfaces. We design the energy function such that cortical fissure (sulci) points on the model are attracted to fissure points on the image and the remaining model points are attracted to the brain surface. A conjugate gradient method minimizes the energy function, allowing the model to automatically converge to the smoothed brain surface. Finally, labels are propagated from the deformed atlas onto the high-resolution brain surface.Index Terms-Brain atlas, deformable surface models, feature extraction, matching.

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Cortical surface modeling reveals gross morphometric correlates of individual differences

Loftus

Tramo

Gazzaniga

1995

Human Brain Mapping

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Advances in human neurobiology are now made possible through methods which combine structural magnetic resonance imaging (MRI), three-dimensional reconstruction, and statistical analysis. MRI-based reconstruction enables the in vivo quantification of regional cortical surface area (rCSA) while inter-group comparisons uncover relationships of cortical morphometry with genotype, sex, and developmental abnormalities. In studies on normals we have found strong associations between the rCSA of monozygotic twins as compared to unrelated pairings. Further analysis of this data uncovered significant differences between the male and female twins in left hemisphere rCSA. When these methods were applied to brains of dyslexic subjects and controls, we identified a pattern of differences involving all major subdivisions of both hemispheres. Taken together, these techniques can illuminate structurefunction issues in both normal and diseased brains. o 1996 Wiley-Liss, Inc.

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Applications of computer graphics and image processing to 2D and 3D modeling of the functional architecture of visual cortex

Cited by 32 publications

References 20 publications

Cat and monkey cortical columnar patterns modeled by bandpass-filtered 2D white noise

Cat and monkey cortical columnar patterns modeled by bandpass-filtered 2D white noise

Surface-based labeling of cortical anatomy using a deformable atlas

Cortical surface modeling reveals gross morphometric correlates of individual differences

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