US 7,680,312 B2 · Method for knowledge based image segmentation using shape models

Abstract

A method for segmenting an object of interest from an image of a patient having such object. Each one of a plurality of training shapes is distorted to overlay a reference shape with a parameter Θi being a measure of the amount of distortion required to effect the overlay. A vector of the parameters Θi is obtained for every one of the training shapes through the minimization of a cost function along with an estimate of uncertainty for every one of the obtained vectors of parameters Θi, such uncertainty being quantified as a covariance matrix Σi. A statistical model represented as {circumflex over (f)}H (Θ,Σ) is generated with the sum of kernels having a mean Θi and covariance Σi. The desired object of interest in the image of the patient is identified by positioning of the reference shape on the image and distorting the reference shape to overlay the obtained image with a parameter Θ being a measure of the amount of distortion required to effect the overlay. An uncertainty is quantified as a covariance matrix Σ and an energy function E=Eshape+Eimage is computed to obtain the probability of the current shape in the statistical shape model Eshape(Θ,Σ)=−log({circumflex over (f)}H) and the fit in the image Eimage.

Claims9

Dependent claims

A method for use in segmenting an object of interest from an image of a patient having such object, comprising:

using medical imaging apparatus to perform the steps of:
transforming a generated reference shape of the object to match every one of a plurality of training shapes according to an energy function comprising distorting each one of the training shapes to overlay the reference shape with a parameter Θi, being a measure of the amount of distortion required at each grid point to effect the overlay for the ith one of the N training shapes;
obtaining a vector of the parameters Θi, for every one of the training shapes through the minimization of a cost function;
estimating an uncertainty for every one of the obtained vectors of parameters Θi, such uncertainty being quantified as a covariance matrix Σi;
providing for the plurality of training shapes a statistical model represented as {circumflex over (f)}H(Θ,Σ) which is the sum of the K Gaussian kernels having a mean Θiand covariance Σi.

2 more elements

A method for learning deformations of an object of interest obtained from an image bank of a patient having such object, comprising:

using medical imaging apparatus to perform the steps of:
generating an initial reference shape of the object of interest to be segmented;
obtaining a predetermined number of, N, images of the desired object from over a general population of such objects, where N is greater than 1;
transforming the generated reference shape to match every one of N training shapes according to an energy function comprising distorting each one of the N training shapes to overlay the reference shape with a parameter Θi being a measure of the amount of distortion required at each grid point to effect the overlay for the ith one of the N training shapes;
obtaining a vector of the parameters Θi, for every one of the N training shapes through the minimization of a cost function;
estimating an uncertainty for every one of the obtained N vectors of parameters Θi, such uncertainty being quantified as a covariance matrix Σi;
providing a statistical model represented as {circumflex over (f)}H(Θ,Σ) cumulating information of the N training shapes modeled with kernels having a mean Θi, and covariance Σi.

4 more elements

Dependencies are inferred from the claim text.

Method for knowledge based image segmentation using shape models

Life of this patent

Method for knowledge based image segmentation using shape models

Life of this patent

Abstract

Claims9