REPRODUCIBLE OBJECTIVE QUANTIFICATION METHOD TO SEGMENT WHITE MATTER STRUCTURES

Patent №

US 8,077,937

Granted

2011-12-13

Filed 2005

Owner

CORNELL RESEARCH FOUNDATION, INC.

Lab

AI components

1

vision

Assignment

Recorded

Dataset

AIPD

2023_r1 edition

Application

11282270

The invention provides a reproducible, objective quantification technique that reliably segments white matter structures. The technique receives a seed voxel within the white matter structure from an individual, determines thresholds and selection criteria, creates a binary mask based on the at least one threshold and the at least one selection criteria and calculates the boundary of the white matter structure based on the binary mask. A magnification factor is applied to each component of the eigenvectors of voxels. Boundary voxels are determined wherein each of the boundary voxels has a magnitude above a predetermined value and is located next to a voxel having a magnitude below the predetermined value. A vector is drawn from the seed voxel to a boundary voxel and the boundary voxels are connected together, thereby forming the region of interest within the connected boundary voxels.

VisionG06V 10/25G06V 10/267G06V 2201/031

AI classification

Vision1.00
AI hardware0.46
Knowledge representation0.15
Speech0.13
Machine learning0.06
Planning0.00
Natural language0.00
Evolutionary computation0.00

Ownership

CORNELL RESEARCH FOUNDATION, INC.

assignment · 171620843

Assignors

NIOGI, SUMIT NARAYAN, MCCANDLISS, BRUCE D.

On an employer assignment, the assignors are typically the inventors.

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