Biomedical subjects

Daniel Freedman

Publications and source records attributed to Daniel Freedman.

3 recordsLinked to original sources

PubMed2005

Model-based segmentation of medical imagery by matching distributions.

The segmentation of deformable objects from three-dimensional (3-D) images is an important and challenging problem, especially in the context of medical imagery. We present a new segmentation algorithm based on matching probability distributions of photometric variables that incorporates learned shape and appearance models for the objects of interest. The main innovation over similar approaches is that there is no need to compute a pixelwise correspondence between the model and the image. This allows for a fast, principled algorithm. We present promising results on difficult imagery for 3-D computed tomography images of the male pelvis for the purpose of image-guided radiotherapy of the prostate.

Algorithms↗

PubMed2005

Improving performance of distribution tracking through background mismatch.

This paper proposes a new density matching method based on background mismatching for tracking of nonrigid moving objects. The new tracking method extends the idea behind the original density-matching tracker, which tracks an object by finding a contour in which the photometric density sampled from the enclosed region most closely matches a model density. This method can be quite sensitive to the initial curve placements and model density. The new method eliminates these sensitivities by adding a second term to the optimization: The mismatch between the model density and the density sampled from the background. By maximizing this term, the tracking algorithm becomes significantly more robust in practice. Furthermore, we show the enhanced ability of the algorithm to deal with target objects which possess smooth or diffuse boundaries. The tracker is in the form of a partial differential equation, and is implemented using the level-set framework. Experiments on synthesized images and real video sequences show our proposed methods are effective and robust; the results are compared with several existing methods.

Algorithms↗

PubMed2004

Active contours for tracking distributions.

A new approach to tracking using active contours is presented. The class of objects to be tracked is assumed to be characterized by a probability distribution over some variable, such as intensity, color, or texture. The goal of the algorithm is to find the region within the current image, such that the sample distribution of the interior of the region most closely matches the model distribution. Two separate criteria for matching distributions are examined, and the curve evolution equations are derived in each case. The flows are shown to perform well in experiments.

Algorithms↗