Fast parametric non-rigid image registration based on feature correspondences

• US 7,596,283 B2
• Filed: 04/05/2005
• Issued: 09/29/2009
• Est. Priority Date: 04/12/2004
• Status: Expired due to Fees

• Current Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Original Assignee: Siemens Medical Solutions USA Incorporated (Siemens AG)
• Inventors: Huang, Xiaolei Sharon, Sun, Yiyong, Xu, Chenyang
• Primary Examiner(s): Johns; Andrew W.
• Assistant Examiner(s): Allison; Andrae S
• Application Number:
  US11/099,143
• Publication Number:
  US 20050249434A1
• Time in Patent Office: 1,638 Days
• Field of Search: 382/128, 382/130, 382/131, 382/294, 382/220, 600/411
• US Class Current: 382/294
• CPC Class Codes: G06V 10/757 Matching configurations of points or features

Abstract

A method and system for non-rigidly registering a fixed to a moving image utilizing a B-Spline based free form deformation (FFD) model is disclosed. The methodology utilizes sparse feature correspondences to estimate an elastic deformation field in a closed form. In a multi-resolution manner, the method is able to recover small to large non-rigid deformations. The resulting deformation field is globally smooth and guarantees one-to-one mapping between the images being registered. The method generally comprises the steps of: detecting feature points on the fixed image and feature points on the moving image; assigning a feature vector to each feature point; calculating the dissimilarity of each pair of feature vectors for feature pairs on the fixed image and the moving image; calculating the correspondence between feature pairs based on the dissimilarity measure; solving for a dense deformation field P using a closed form FFD model; and transforming the moving image and the feature points on the moving image using a current FFD deformation field estimate.

Claims

1. In a computer, a method of non-rigid image registration between a fixed image and a moving image, comprising the steps of:

(a) detecting feature points on the fixed image and feature points on the moving image;

(b) assigning a feature vector to each feature point;

(c) calculating the dissimilarity of each pair of feature vectors for feature pairs on the fixed image and the moving image;

(d) calculating the correspondence between feature pairs based on dissimilarity measure;

Xi and Xi′

;

(e) solving for a dense deformation field P using a closed form free from deformation (FFD) model; and

(f) transforming the moving image and the feature points on the moving image using a current FFD deformation field estimate.

2. The method recited in claim 1, further comprising repeating steps (b) through (d) to minimize the deformation field estimate.

3. The method recited in claim 1, wherein the selected feature points have the highest local entropy.

4. The method recited in claim 1, wherein given a regular lattice of control points:

P_m,n=(P_m,n^x,P_m,n^y);

m=1, . . . , M,n=1, . . . ,N

overlaid to an image region

Γ

={x}={(x,y)|1≦

x≦

X,1≦

y≦

Y}

the initial configuration of a control lattice is denoted as P⁰, and the deforming control lattice as P=P⁰+dP, where the FFD parameters are the deformations of the control points in both directions (xy);

Θ

={(δ

P_m,n^x,δ

P_m,n^y)};

(m,n)∈

[1,M]×

[1,N]

and given the deformation of the control lattice from P⁰ to P, the deformed location L(x)=(x′

,y′

) of a pixel x=(x,y), is defined in terms of a tensor product of a Cubic B-spline;

5. The method recited in claim 4, wherein x_i′

is assumed to be the deformed location L(x), and the relationship between the two point sets can be represented as follows;

U=Sp

where, U is the displacement matrix between the correspondence pairs;

6. A computer readable memory medium containing program instructions which, when executed by a processor, enable a computer to non-rigidly register a fixed image to a moving image, by;

(a) detecting feature points on the fixed image and feature points on the moving image;

(b) assigning a feature vector to each feature point;

(c) calculating the dissimilarity of each pair of feature vectors for feature pairs on the fixed image and the moving image;

(d) calculating the correspondence between feature pairs based on dissimilarity measure;

Xi and Xi′

;

(e) solving for a dense deformation field P using a closed form free form deformation (FFD) model; and

(f) transforming the moving image and the feature points on the moving image using a current FFD deformation field estimate.

7. The computer readable memory medium recited in claim 6, further comprising program instructions which, when executed by the processor, repeat steps (c) through (f) to minimize the deformation field estimate.

8. The computer readable memory medium recited in claim 6, wherein given a regular lattice of control points:

P_m,n=(P_m,n^x,P_m,n^y);

m=1, . . . , M,n=1, . . . ,N

overlaid to an image region

Γ

={x}={(x,y)|1≦

x≦

X,1≦

y≦

Y}

the initial configuration of a control lattice is denoted at P⁰, and the deforming control lattice as P=P⁰+dP, where the FFD parameters are the deformations of the control points in both directions (x,y);

Θ

={(δ

P_m,n^x,δ

P_m,n^y)};

(m,n)∈

[1,M]×

[1,N]

and given the deformation of the control lattice from P⁰ to P, the deformed location L(x)=(x′

,y′

) of a pixel x=(x,y), is defined in terms of a tensor product of a Cubic B-spline;

9. The computer readable medium recited in claim 8, wherein x_i′

is assumed to be the deformed location L(x), and the relationship between the two point sets can be represented as follows;

U=Sp

where, U is the displacement matrix between the correspondence pairs;