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Überatlas: Robust speed-up of feature-based registration and multi-atlas based segmentation

Alvén, Jennifer LU (2015) In Master’s Theses in Mathematical Sciences FMA820 20141
Mathematics (Faculty of Engineering)
Abstract
Registration is a key component in multi-atlas approaches to medical image segmentation. Current state-of-the-art uses intensity-based registration methods, but such methods tend to be slow and sensitive to large amount of noise and anatomical abnormalities present in medical images. In this master thesis, a novel feature-based registration method is presented and compared to two baseline methods; an intensity-based and a feature-based. The registration method is implemented with the purpose to handle outliers in a robust way and be faster than the two baselines. The algorithm performs a multi-atlas based segmentation by first co-registering the atlases and clustering the feature points in an Uberatlas, and then registering the feature... (More)
Registration is a key component in multi-atlas approaches to medical image segmentation. Current state-of-the-art uses intensity-based registration methods, but such methods tend to be slow and sensitive to large amount of noise and anatomical abnormalities present in medical images. In this master thesis, a novel feature-based registration method is presented and compared to two baseline methods; an intensity-based and a feature-based. The registration method is implemented with the purpose to handle outliers in a robust way and be faster than the two baselines. The algorithm performs a multi-atlas based segmentation by first co-registering the atlases and clustering the feature points in an Uberatlas, and then registering the feature clusters to a target image. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain, with corresponding gold standard. The method produces comparable segmentation results to the two baseline methods and reduces the run-time significantly. (Less)
Please use this url to cite or link to this publication:
author
Alvén, Jennifer LU
supervisor
organization
course
FMA820 20141
year
type
H2 - Master's Degree (Two Years)
subject
keywords
computer vision, medical image analysis, feature-based registration, segmentation of pericardium, segmentation of brain, multi-atlas based segmentation
publication/series
Master’s Theses in Mathematical Sciences
report number
LUTFMA-3270-2015
ISSN
1404-6342
other publication id
2015:E1
language
English
id
4935026
date added to LUP
2015-01-26 13:02:34
date last changed
2015-01-26 13:17:11
@misc{4935026,
  abstract     = {Registration is a key component in multi-atlas approaches to medical image segmentation. Current state-of-the-art uses intensity-based registration methods, but such methods tend to be slow and sensitive to large amount of noise and anatomical abnormalities present in medical images. In this master thesis, a novel feature-based registration method is presented and compared to two baseline methods; an intensity-based and a feature-based. The registration method is implemented with the purpose to handle outliers in a robust way and be faster than the two baselines. The algorithm performs a multi-atlas based segmentation by first co-registering the atlases and clustering the feature points in an Uberatlas, and then registering the feature clusters to a target image. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain, with corresponding gold standard. The method produces comparable segmentation results to the two baseline methods and reduces the run-time significantly.},
  author       = {Alvén, Jennifer},
  issn         = {1404-6342},
  keyword      = {computer vision,medical image analysis,feature-based registration,segmentation of pericardium,segmentation of brain,multi-atlas based segmentation},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master’s Theses in Mathematical Sciences},
  title        = {Überatlas: Robust speed-up of feature-based registration and multi-atlas based segmentation},
  year         = {2015},
}