Medical Imaging

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 a method for vessel enhancement filtering which relies on local structure. Using information about the second order ellipsoid, this is an improvement over previous works that exhibits robustness to noise and background for vessel enhancement in the experiments with two clinical image modalities - 2D DSA images and 3D MRA images. ...

Introduction This paper presents a graph cut approach to the image segmentation task. Considering the image to be a directed graph with two nodes representing the source (object) and the sink (background), the authors propose a combinatorial optimization framework for image segmentation using $s/t$ graph cuts. This is the first global optimization object extraction technique that is extensible to beyond 2-D images. ...