Siemens Medical Solutions USA, Inc.

Thomas Stingl

Mariappan S Nadar

Daphne Yu

Jeffrey Paul Johnson

John Patrick Collins

Marc Rose

John Scott Nafziger

Carlos E Cardenas

Artifact quantification is provided in volume rendering. Since the visual conspicuity of rendering artifacts strongly influences subjective assessments of image quality, quantitative metrics that accurately correlate with human visual perception may provide consistent values over a range of imaging conditions.

Perception-based artifact quantification for volume rendering

A seed point is selected inside a structure that is to be segmented in image data. An adaptive model is defined around the seed point, and a preprocessing filter is applied only within the bounding region. A presegmentation of the preprocessed result is performed, and the bounding region is expanded if necessary to accommodate the presegmentation result. An adaptive model for post-processing may be used. The model is translated, rotated and scaled to find a best fit with the pre-segmented data.

Multidimensional segmentation based on adaptive bounding box and ellipsoid models

Growing community of inventors

Erlangen, Germany

Thomas Stingl