Karl V. Steiner

FuzzyMatte: A computationally efficient scheme for interactive matting

In this paper, we propose an online interactive matting algorithm, which we call FuzzyMatte. Our framework is based on computing the fuzzy connectedness (FC) from each unknown pixel to the known foreground and background. FC effectively captures the adjacency and similarity between image elements and can be efficiently computed using the strongest connected path searching algorithm. The final alpha value at each pixel can then be calculated from its FC. While many previous methods need to completely recompute the matte when new inputs are provided, FuzzyMatte effectively integrates these new inputs with the previously estimated matte by efficiently recomputing the FC value for a small subset of pixels. Thus, the computational overhead between each iteration of the refinement is significantly reduced. We demonstrate FuzzyMatte on a wide range of images. We show that FuzzyMatte updates the matte in an online interactive setting and generates high quality matte for complex images.

Lung Nodule Growth Analysis from 3D CT Data with a Coupled Segmentation and Registration Framework

In this paper we propose a new framework to simultaneously segment and register lung and tumor in serial CT data. Our method assumes nonrigid transformation on lung deformation and rigid structure on the tumor. We use the B- Spline-based nonrigid transformation to model the lung deformation while imposing rigid transformation on the tumor to preserve the volume and the shape of the tumor. In particular, we set the control points within the tumor to form a control mesh and thus assume the tumor region follows the same rigid transformation as the control mesh. For segmentation, we apply a 2D graph-cut algorithm on the 3D lung and tumor datasets. By iteratively performing segmentation and registration, our method achieves highly accurate segmentation and registration on serial CT data. Finally, since our method eliminates the possible volume variations of the tumor during registration, we can further estimate accurately the tumor growth, an important evidence in lung cancer diagnosis. Initial experiments on five sets of patients serial CT data show that our method is robust and reliable.

Estimation of Ground-Glass Opacity Measurement in CT Lung Images

We propose to measure quantitatively the opacity property of each pixel in a ground-glass opacity tumor from CT images. Our method results in an opacity map in which each pixel takes opacity value of [0-1]. Given a CT image, our method accomplishes the estimation by constructing a graph Laplacian matrix and solving a linear equations system, with assistance from some manually drawn scribbles for which the opacity values are easy to determine manually. Our method resists noise and is capable of eliminating the negative influence of vessels and other lung parenchyma. Experiments on 40 selected CT slices of 11 patients demonstrate the effectiveness of this technique. The opacity map produced by our method is invaluable in practice. From this map, many features can be extracted to describe the spatial distribution pattern of opacity and used in a computer-aided diagnosis system.