What is claimed is:
1. A method of determining alignment of decorelating images in high dimensional feature space, said method comprising: simultaneously registering a source image of areference modality to a plurality of target images of a second modality with an algorithm based upon a measure of information affinity present in both of the source and target images to create a registered image; extracting a plurality of featurevectors from the registered image for each of the source and target images; plotting a distribution of the feature vectors on an entropic graph; determining edge lengths between the feature vectors from the entropic graph; and determining a similaritymeasure of one of an α-divergence estimate or an α-affinity estimate based upon these edge lengths to indicate whether the source and target images are sufficiently registered.
2. A method as set forth in claim 1 wherein the entropic graph is further defined as a minimal graph spanning the feature vectors that minimizes a function of the total edge length of the minimal graph and that approximates the α-affinityor α-divergence of the distribution of the feature vectors.
3. A method as set forth in claim 1 wherein the entropic graph is further defined as based upon one of a minimum spanning tree (MST) or a k-nearest neighbor graph (k-NNG), a Steiner tree, a Delaunay triangulation, or a traveling salesmanproblem (TSP).
4. A method as set forth in claim 1 further comprising the step of re-deforming the source image into the target images with a different algorithm to shorten the edge lengths from the entropic graph thereby improving the similarity measuretherebetween.
5. A method as set forth in claim 1 wherein the reference modality is further defined as different than the second modality.
6. A method as set forth in claim 1 wherein each of the feature vectors represent at least two feature dimensions.
7. A method as set forth in claim 1 wherein each of the feature vectors represent more than two feature dimensions.
8. A method as set forth in claim 1 wherein the step of determining the similarity measure is further defined as utilizing at least one of an α-mutual information (α-MI), an α-geometric-arithmetic (α-GA) divergence, anda Henze-Penrose (HP) divergence.
9. A method as set forth in claim 8 wherein the α-MI is further defined by the general formula: α××α×α××∫α.fun-ction.×αƒ×αƒ×d×d ##EQU00015## wherein f and g are densities, α ε (0,1)1, f(x,y) is a joint density, and g(x,y) is a product of marginals f(x)f(y).
10. A method as set forth in claim 8 wherein the α-GA divergence is further defined by the general formula: α×׃α×××α×-×××∫××××α×.time- s.××××××××α×d ##EQU00016## wherein f and g are densities, α ε (0,1)1, p and q=1-p are weights; pε [0,1], f(x,y) is a joint density, and g(x,y) is a product of marginals f(x)f(y).
11. A method as set forth in claim 8 wherein the HP divergence is further defined by the general formula: ×∫׃׃ƒƒ.times- .d ##EQU00017## wherein f and g are densities, p and q=1-p areweights; p ε [0,1], f(x,y) is a joint density, and g(x,y) is a product of marginals f(x)f(y).
12. A method of determining alignment of decorelating images in high dimensional feature space, said method comprising: simultaneously registering more than two images comprising at least a source image of a reference modality to target imagesof a second modality with an algorithm based upon a measure of mutual information present in both of the source and target images to create a registered image; extracting a plurality of feature vectors from the registered image for each of the sourceand target images; determining edge lengths between proximal feature vectors from an entropic graph; and determining a similarity measure of one of an α-divergence estimate or an α-affinity estimate based upon these edge lengths with atleast one of an α-mutual information (α-MI), an α-geometric-arithmetic (α-GA) divergence, and a Henze-Penrose (HP) divergence.
13. A method as set forth in claim 12 wherein the α-MI is further defined by the general formula: α××α×α××∫α.fun-ction.×αƒ×αƒ×d×d ##EQU00018## wherein f and g are densities, α ε (0,1)1, f(x,y) is a joint density, and g(x,y) is a product of marginals f(x)f(y).
14. A method as set forth in claim 12 wherein the α-GA divergence is further defined by the general formula: α×׃α×××α×-×∫ƒƒα׃××.fu- nction.α×d ##EQU00019## wherein f and g are densities, α ε (0,1)1, p and q=1-p are weights; p ε [0,1], f(x,y) is a joint density, andg(x,y) is a product of marginals f(x)f(y).
15. A method as set forth in claim 12 wherein the HP divergence is further defined by the general formula: ×∫׃׃ƒƒ.times- .d ##EQU00020## wherein f and g are densities, p and q=1-p areweights; p ε [0,1], f(x,y) is a joint density, and g(x,y) is a product of marginals f(x)f(y).
16. A method as set forth in claim 12 wherein the entropic graph is further defined as based upon one of a minimum spanning tree (MST) or a k-nearest neighbor graph (k-NNG).
17. A computer readable recording medium storing an executable control program for executing a method of determining alignment of decorelating images in high dimensional feature space, said method comprising: simultaneously registering morethan two images with an algorithm based upon a measure of information affinity present in the images to create a registered image; extracting a plurality of feature vectors from the registered image for each of the source and target images; determiningedge lengths between proximal feature vectors from an entropic graph; and determining a similarity measure of one of an α-divergence estimate or an α-affinity estimate based upon these edge lengths to indicate whether the source and targetimages are sufficiently registered.
18. A method as set forth in claim 17 wherein the entropic graph is further defined as based upon one of a minimum spanning tree (MST), a k-nearest neighbor graph (k-NNG), a Steiner tree, a Delaunay triangulation, or a traveling salesmanproblem (TSP).
19. A method as set forth in claim 17 wherein the step of determining the similarity measure is further defined as utilizing at least one of an α-mutual information (α-MI), an α-geometric-arithmetic (α-GA) divergence,and a Henze-Penrose (HP) divergence.
