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Building generic anatomical models using virtual model cutting and iterative registration
BACKGROUND: Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limite...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2010
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2830992/ https://www.ncbi.nlm.nih.gov/pubmed/20144190 http://dx.doi.org/10.1186/1471-2342-10-5 |
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author | Xiao, Mei Soh, Jung Meruvia-Pastor, Oscar Schmidt, Eric Hallgrímsson, Benedikt Sensen, Christoph W |
author_facet | Xiao, Mei Soh, Jung Meruvia-Pastor, Oscar Schmidt, Eric Hallgrímsson, Benedikt Sensen, Christoph W |
author_sort | Xiao, Mei |
collection | PubMed |
description | BACKGROUND: Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limited views of them that medical images can offer, it is still an exceptionally difficult task to quickly and accurately create 3D generic models (a model is a 3D graphical representation of a biological structure) based on medical image stacks (a stack is an ordered collection of 2D images). We show that the creation of a generic model that captures spatial information exploitable in statistical analyses is facilitated by coupling our generalized segmentation method to existing automatic image registration algorithms. METHODS: The method of creating generic 3D models consists of the following processing steps: (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) interactively extracting sub-volume by cutting each model to generate the sub-model of interest; (iv) creating image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models. RESULTS: After several registration procedures are applied to the image stacks, we can create averaged image stacks with sharp boundaries. The averaged 3D model created from those image stacks is very close to the average representation of the population. The image registration time varies depending on the image size and the desired accuracy of the registration. Both volumetric data and surface model for the generic 3D model are created at the final step. CONCLUSIONS: Our method is very flexible and easy to use such that anyone can use image stacks to create models and retrieve a sub-region from it at their ease. Java-based implementation allows our method to be used on various visualization systems including personal computers, workstations, computers equipped with stereo displays, and even virtual reality rooms such as the CAVE Automated Virtual Environment. The technique allows biologists to build generic 3D models of their interest quickly and accurately. |
format | Text |
id | pubmed-2830992 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-28309922010-03-03 Building generic anatomical models using virtual model cutting and iterative registration Xiao, Mei Soh, Jung Meruvia-Pastor, Oscar Schmidt, Eric Hallgrímsson, Benedikt Sensen, Christoph W BMC Med Imaging Research article BACKGROUND: Using 3D generic models to statistically analyze trends in biological structure changes is an important tool in morphometrics research. Therefore, 3D generic models built for a range of populations are in high demand. However, due to the complexity of biological structures and the limited views of them that medical images can offer, it is still an exceptionally difficult task to quickly and accurately create 3D generic models (a model is a 3D graphical representation of a biological structure) based on medical image stacks (a stack is an ordered collection of 2D images). We show that the creation of a generic model that captures spatial information exploitable in statistical analyses is facilitated by coupling our generalized segmentation method to existing automatic image registration algorithms. METHODS: The method of creating generic 3D models consists of the following processing steps: (i) scanning subjects to obtain image stacks; (ii) creating individual 3D models from the stacks; (iii) interactively extracting sub-volume by cutting each model to generate the sub-model of interest; (iv) creating image stacks that contain only the information pertaining to the sub-models; (v) iteratively registering the corresponding new 2D image stacks; (vi) averaging the newly created sub-models based on intensity to produce the generic model from all the individual sub-models. RESULTS: After several registration procedures are applied to the image stacks, we can create averaged image stacks with sharp boundaries. The averaged 3D model created from those image stacks is very close to the average representation of the population. The image registration time varies depending on the image size and the desired accuracy of the registration. Both volumetric data and surface model for the generic 3D model are created at the final step. CONCLUSIONS: Our method is very flexible and easy to use such that anyone can use image stacks to create models and retrieve a sub-region from it at their ease. Java-based implementation allows our method to be used on various visualization systems including personal computers, workstations, computers equipped with stereo displays, and even virtual reality rooms such as the CAVE Automated Virtual Environment. The technique allows biologists to build generic 3D models of their interest quickly and accurately. BioMed Central 2010-02-08 /pmc/articles/PMC2830992/ /pubmed/20144190 http://dx.doi.org/10.1186/1471-2342-10-5 Text en Copyright ©2010 Xiao et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research article Xiao, Mei Soh, Jung Meruvia-Pastor, Oscar Schmidt, Eric Hallgrímsson, Benedikt Sensen, Christoph W Building generic anatomical models using virtual model cutting and iterative registration |
title | Building generic anatomical models using virtual model cutting and iterative registration |
title_full | Building generic anatomical models using virtual model cutting and iterative registration |
title_fullStr | Building generic anatomical models using virtual model cutting and iterative registration |
title_full_unstemmed | Building generic anatomical models using virtual model cutting and iterative registration |
title_short | Building generic anatomical models using virtual model cutting and iterative registration |
title_sort | building generic anatomical models using virtual model cutting and iterative registration |
topic | Research article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2830992/ https://www.ncbi.nlm.nih.gov/pubmed/20144190 http://dx.doi.org/10.1186/1471-2342-10-5 |
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