Machine Learning

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. ...

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. ...

Introduction This paper presents a novel interactive tool for efficient and reproducible boundary extraction with minimal user input. Despite the user not being very accuracte with the manual marking of the seed points, the algorithm snaps the boundary to the nearest strong object edge. Unlike active contour models where the user is unaware of how the final boundary will look like after energy minimization (which, if unsatisfactory, requires the entire process to be repeated again), this algorithm is interactive and therefore the user is aware of the “live-wire boundary snapping”. Moreover, “boundary cooling” and “on-the-fly training” are two novel contributions of the paper which help reduce user input while maintaining the stability of the boundary. ...