Work

Shells and Spheres: An n‐Dimensional Framework for Medial‐Based Image Segmentation

Year: 2010

Type: article

Abstract: We have developed a method for extracting anatomical shape models from n-dimensional images using an image analysis framework we call Shells and Spheres. This framework utilizes a set of spherical operators centered at each image pixel, grown to reach, but not cross, the nearest object boundary by incorporating "shells" of pixel intensity values while analyzing intensity mean, variance, and first-order moment. Pairs of spheres on opposite sides of putative boundaries are then analyzed to determine boundary reflectance which is used to further constrain sphere size, establishing a consensus as to boundary location. The centers of a subset of spheres identified as medial (touching at least two boundaries) are connected to identify the interior of a particular anatomical structure. For the automated 3D algorithm, the only manual interaction consists of tracing a single contour on a 2D slice to optimize parameters, and identifying an initial point within the target structure.

Source: International Journal of Biomedical Imaging

DOI: 10.1155/2010/980872

Authors: Aaron Cois, John Galeotti, Robert Tamburo, Michael Sacks, George Stetten

Institution: University of Pittsburgh

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,000

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Biomedical Imaging and Bioengineering, U.S. National Library of Medicine