System and method for a contiguous support vector machine
First Claim
Patent Images
1. A method of classifying features in digitized images comprising the steps of:
- providing a plurality of feature points in an n-dimensional space, wherein said feature points have been extracted from a digitized medical image;
formulating a support vector machine to classify said feature point into one of two sets, wherein each said feature classification vector is transformed by an adjacency matrix defined by those points that are nearest neighbors of said feature; and
solving said support vector machine by a linear optimization algorithm to determine a classifying plane that separates the feature vectors into said two sets.
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Abstract
A method of classifying features in digitized images includes providing a plurality of feature points in an n-dimensional space, wherein said feature points have been extracted from a digitized medical image, formulating a support vector machine to classify said feature point into one of two sets, wherein each said feature classification vector is transformed by an adjacency matrix defined by those points that are nearest neighbors of said feature, and solving said support vector machine by a linear optimization algorithm to determine a classifying plane that separates the feature vectors into said two sets.
31 Citations
20 Claims
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1. A method of classifying features in digitized images comprising the steps of:
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providing a plurality of feature points in an n-dimensional space, wherein said feature points have been extracted from a digitized medical image;
formulating a support vector machine to classify said feature point into one of two sets, wherein each said feature classification vector is transformed by an adjacency matrix defined by those points that are nearest neighbors of said feature; and
solving said support vector machine by a linear optimization algorithm to determine a classifying plane that separates the feature vectors into said two sets. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of classifying features in digitized images comprising the steps of:
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providing a plurality of digitized images, wherein each said image comprises a set of intensities defined on a lattice of points, a spatially registering each of said images by estimating an affine transformation between the images;
normalizing the intensities of each of said images by application of an affine transformation to said intensities;
extracting a plurality of feature points from said digitized images; and
transforming each said feature by an adjacency matrix R defined by a similarity function r among any two features (fi,fj) wherein a matrix element Rij is defined by Rij=r(fi,fj)ε
{0,1}, i,jε
{1, . . . ,n}, wherein n is a number of features, wherein spatial information is incorporated into each said feature. - View Dependent Claims (11)
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12. A program storage device readable by a computer, tangibly embodying a program of instructions executable by the computer to perform the method steps for classifying features in digitized images, said method comprising the steps of:
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providing a plurality of feature points in an n-dimensional space, wherein said feature points have been extracted from a digitized medical image;
formulating a support vector machine to classify said feature point into one of two sets, wherein each said feature classification vector is transformed by an adjacency matrix defined by those points that are nearest neighbors of said feature; and
solving said support vector machine by a linear optimization algorithm to determine a classifying plane that separates the feature vectors into said two sets. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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Specification