Segmentation of Lumbar Intervertebral Discs from High-Resolution 3D MR Images Using Multi-level Statistical Shape Models

2014 ◽  
pp. 173-183 ◽  
Author(s):  
Aleš Neubert ◽  
Jurgen Fripp ◽  
Craig Engstrom ◽  
Stuart Crozier
Keyword(s):  
High Resolution ◽  
Intervertebral Discs ◽  
Mr Images ◽  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape ◽  
Multi Level

Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models

2012 ◽  
Vol 57 (24) ◽  
pp. 8357-8376 ◽  
Author(s):  
A Neubert ◽  
J Fripp ◽  
C Engstrom ◽  
R Schwarz ◽  
L Lauer ◽  
...  
Keyword(s):  
High Resolution ◽  
Automated Detection ◽  
Mr Images ◽  
3D Segmentation ◽  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape ◽  
Spine Mr

scholarly journals Construction of Statistical Shape Models from Minimal Deformations

2008 ◽  
Author(s):  
Darko Zikic ◽  
Ben Glocker ◽  
Micheal Sass Hansen ◽  
Ali Khamene ◽  
Nassir Navab
Keyword(s):  
Linear Part ◽  
Least Square ◽  
Free Form ◽  
Control Points ◽  
Mr Images ◽  
Data Set ◽  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape ◽  
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Abstract. Statistical shape models (SSM) capture the variation of shape across a population, in order to allow further analysis. Previous work demonstrates that deformation fields contain global transformation components, even if global pre- registration is performed. It is crucial to construction of SSMs to remove these global transformation components from the local deformations - thus obtaining minimal deformations - prior to using these as input for SSM construction. In medical image processing, parameterized SSMs based on control points of free-form deformations (FFD) are a popular choice, since they offer several advantages compared to SSMs based on dense deformation fields. In this work, we extend the previous approach by presenting a framework for construction of both, unparameterized and FFD-based SSMs from minimal deformations. The core of the method is computation of minimal deformations by extraction of the linear part from the original dense deformations. For FFD-based SSMs, the FFD-parameterization of the minimal deformations is performed by projection onto the space of FFDs. Both steps are computed by close-form solutions optimally in the least-square sense. The proposed method is evaluated on a data set of 62 MR images of the corpus callosum. The results show a significant improvement achieved by the proposed method for SSMs built on dense fields, as well as on FFD-based SSMs.

A framework for the merging of pre-existing and correspondenceless 3D statistical shape models

2014 ◽  
Vol 18 (7) ◽  
pp. 1044-1058 ◽  
Author(s):  
Marco Pereañez ◽  
Karim Lekadir ◽  
Constantine Butakoff ◽  
Corné Hoogendoorn ◽  
Alejandro F. Frangi
Keyword(s):  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape

3D Statistical Shape Models Using Direct Optimisation of Description Length

2002 ◽  
pp. 3-20 ◽  
Author(s):  
Rhodri H. Davies ◽  
Carole J. Twining ◽  
Tim F. Cootes ◽  
John C. Waterton ◽  
Chris J. Taylor
Keyword(s):  
Statistical Shape Models ◽  
Shape Models ◽  
Statistical Shape
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