Mutual Information Given two discrete random variables $A$ and $B$ with pardinal probability distributions $p_A(a)$ and $p_B(b)$ and joint probability distribution $p_{AB}(a,b)$, the two variables are said to be statistically independent is $p_{AB}(a,b) = p_A(a).p_B(b)$, and are said to be maximally dependent if they are related by a one-to-one mapping $T$ such that $p_A(a) = p_B(T(a)) = p_{AB}(a, T(a))$. The mutual information $I(A,B)$ represents the degree of dependence of A and B ...

Introduction This paper presents an algorithm for non-rigid registration formulated as a discrete labeling problem. The authors note that the two major contemporary works for image registration had inherent flaws - Free-Form Deformation (FFD) based model was crippled by the choice of the set of control points to represent the deformation, while Demons Based Method did not penalize large displacements of pixels and was highly sensitive to local artifacts. The authors demonstrate the proposed algorithm’s superior performance for 2D and 3D registration compared to the two aforementioned algorithms. ...

Introduction This paper presents an algorithm for non-rigid registration of contrast-enhanced breast MR image sequences. The authors propose a model incorporating both global transformations (represented by affine transformation) as well as local transformation (free-form deformation represented using B-splines). Normalized mutual information was used as the similarity measure across images. The authors demonstrate the algorithm’s superior performance compared to the rigid and affine registration techniques. ...

Introduction This paper presents an algorithm for generating statistical shape models by addressing it as a correspondence problem of finding the parameterization of each shape in the training set, instead of manually annotating a set of “landmark” points for each image in the training set. The authors demonstrate the robustness of the algorithm by applying it to a variety of training image sets - infarcts, kidneys, knee cartilages, hand outlines, hip prostheses, and left ventricles. The proposed minimum description length model leads to good compactness, specificity and generalizes well, outperforming the contemporary gold standard - manual landmarking. Moreover, the authors also show that this model can be extended to work with 3-D images. ...

Introduction This paper presents an algorithm for modeling rigid objects in the presence of noise, clutter, and occlusion and overcomes the problems facing the contemporary algorithms - sacrificing specificity to accommodate variability. The proposed models permits deformations only consistent with the class of objects it represents. The authors demonstrate the robustness of the algorithm by applying it to a variety of training image sets - resistors, heart, hand, and worm models. ...