Shells and Spheres: A Framework for Variable Scale Statistical Image Analysis

Constantine Aaron Cois, Ken Rockot, John Galeotti, Robert Joseph Tamburo, and George D. Stetten
tech. report CMU-RI-TR-04-19, Robotics Institute, Carnegie Mellon University, April, 2006


Download
  • Adobe portable document format (pdf) (576KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
We have developed a framework for analyzing images, called Shells and Spheres, based on a set of spheres with adjustable radii, with exactly one sphere centered at each image pixel. This set of spheres, known as a sphere map, is considered optimized when each sphere reaches, but does not cross, the nearest boundary. Calculations denoted as Variable-Scale Statistics (VSS) are performed on populations of pixels within spheres, as well as populations of adjacent and overlapping spheres, in order to deduce the proper radius of each sphere. Spheres grow or shrink by adding or deleting an outer shell one pixel thick. Unlike conventional fixed-scale kernels, our spherical operators consider as many pixels as possible to differentiate between objects and accurately delineate boundaries. We use the word ?sphere? here for brevity, though the approach is not limited to 3D and is valid in n-dimensions. We illustrate our approach on synthetic images containing objects with uniform intensity. We then describe a particular algorithm using Shells and Spheres and demonstrate it by segmenting the aortic arch in a contrast-enhanced CT scan, both in 2D and 3D.

Keywords
image analysis, image segmentation, medial, scale, statistics, distance transform

Notes
Number of pages: 21

Text Reference
Constantine Aaron Cois, Ken Rockot, John Galeotti, Robert Joseph Tamburo, and George D. Stetten, "Shells and Spheres: A Framework for Variable Scale Statistical Image Analysis," tech. report CMU-RI-TR-04-19, Robotics Institute, Carnegie Mellon University, April, 2006

BibTeX Reference
@techreport{Cois_2006_5401,
   author = "Constantine Aaron Cois and Ken Rockot and John Galeotti and Robert Joseph Tamburo and George D Stetten",
   title = "Shells and Spheres: A Framework for Variable Scale Statistical Image Analysis",
   booktitle = "",
   institution = "Robotics Institute",
   month = "April",
   year = "2006",
   number= "CMU-RI-TR-04-19",
   address= "Pittsburgh, PA",
}