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Proceedings Paper

Skeletal shape correspondence via entropy minimization
Author(s): Liyun Tu; Martin Styner; Jared Vicory; Beatriz Paniagua; Juan Carlos Prieto; Dan Yang; Stephen M. Pizer
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Paper Abstract

Purpose: Improving the shape statistics of medical image objects by generating correspondence of interior skeletal points. Data: Synthetic objects and real world lateral ventricles segmented from MR images. Method(s): Each object’s interior is modeled by a skeletal representation called the s-rep, which is a quadrilaterally sampled, folded 2-sided skeletal sheet with spoke vectors proceeding from the sheet to the boundary. The skeleton is divided into three parts: up-side, down-side and fold-curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along their skeletal parts in each training sample so as to tighten the probability distribution on those spokes’ geometric properties while sampling the object interior regularly. As with the surface-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and sampling regularity. The spokes’ geometric properties are skeletal position, spoke length and spoke direction. The properties used to measure the regularity are the volumetric subregions bounded by the spokes, their quadrilateral sub-area and edge lengths on the skeletal surface and on the boundary. Results: Evaluation on synthetic and real world lateral ventricles demonstrated improvement in the performance of statistics using the resulting probability distributions, as compared to methods based on boundary models. The evaluation measures used were generalization, specificity, and compactness. Conclusions: S-rep models with the proposed improved correspondence provide significantly enhanced statistics as compared to standard boundary models.

Paper Details

Date Published: 20 March 2015
PDF: 8 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130U (20 March 2015); doi: 10.1117/12.2081245
Show Author Affiliations
Liyun Tu, Chongqing Univ. (China)
The Univ. of North Carolina at Chapel Hill (United States)
Martin Styner, The Univ. of North Carolina at Chapel Hill (United States)
Jared Vicory, The Univ. of North Carolina at Chapel Hill (United States)
Beatriz Paniagua, The Univ. of North Carolina at Chapel Hill (United States)
Juan Carlos Prieto, Brigham and Women's Hospital (United States)
Dan Yang, Chongqing Univ. (China)
Stephen M. Pizer, The Univ. of North Carolina at Chapel Hill (United States)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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