Method for knowledge based image segmentation using shape models

1. 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 Θ i , being a measure of the amount of distortion required at each grid point to effect the overlay for the i th one of the N training shapes;

obtaining a vector of the parameters Θ i , 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 Θ i , such uncertainty being quantified as a covariance matrix Σ i ;

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 Θ i and covariance Σ i .

2. The method recited in claim 1 wherein the cost function is the sum squared difference between a distance map generated for one of the training shapes and the transformed reference model.

3. The method recited in claim 1 including additionally:

obtaining the image of the desired object from the patient;

identifying the desired object of interest in the image of the patient by positioning of the reference shape on the image comprising:

transforming the generated reference shape to match the obtained image according to the energy function comprising 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;

estimating an uncertainty, such uncertainty being quantified as a covariance matrix Σ;

computing a shape term of an energy function E shape (Θ,Σ)=−log({circumflex over (f)} H ) to obtain the probability of the current shape in the statistical shape model; and

computing an image term of an energy function E image to evaluate the fit of the shape in the image

minimizing the energy function E shape +E image .

4. The method recited in claim 3 wherein the minimizing E shape (Θ,Σ)=−log({circumflex over (f)} H ) comprises:

computing E shape (Θ,Σ Θ )=−log({circumflex over (f)} H ) and E image ;

changing Θ according to the derivative of E shape +E image ;

generating a new covariance matrix as Σ Θ , to thereby compute a new E shape(Θ,Σ Θ )=−log({circumflex over (f)} H ) and E image until E shape (Θ,Σ)+E image is minimized.

5. The method recited in claim 1 wherein the cost function is the distance between such one of the training shapes and the transformed reference model.

6. 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 Θ i being a measure of the amount of distortion required at each grid point to effect the overlay for the i th one of the N training shapes;

obtaining a vector of the parameters Θ i , 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 Θ i , such uncertainty being quantified as a covariance matrix Σ i ;

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 Θ i , and covariance Σ i .

7. The method recited in claim 6 wherein the uncertainty is computed by assuming that the energy function can be approximated at its minimum by a paraboloid, the width of the paraboloid being representative of the degree of uncertainty.

8. The method recited in claim 7 wherein the reference shape is on a reference coordinate system having an overlaid free form deformation grid. Such free form deformation grid having a plurality of grid points with positions represented in the vector Θ, with a the projection of the covariance matrix onto a grid point being represented as an ellipse elongated in the direction of greater uncertainty, and wherein, in the case of smooth linear contours, a component is tangent to the reference shape and another component is normal to the reference shape.

9. The method recited in claim 6 including additionally:

obtaining the image of the desired object from the patient;

identifying the desired object of interest in the image of the patient by positioning of the reference shape on the image comprising:

transforming the generated reference shape to match the obtained image according to the energy function comprising 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;

estimating an uncertainty, such uncertainty being quantified as a covariance matrix Σ;

computing an energy function E shape to obtain the probability of the current shape in the statistical shape model; and

minimizing E=E shape +E image comprising:

computing E shape (Θ,Σ)=−log({circumflex over (f)} H );

computing E image ;

changing Θ;

generating an updated covariant matrix with the changed Θ;

recomputing the energy function E=E shape +E image .