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Semi-Automatic Segmentation of Coronary Arteries in CT Images

Lemic, Anja LU (2016) In Master's Theses in Mathematical Sciences FMA820 20161
Mathematics (Faculty of Engineering)
Abstract
Coronary heart diseases is one of the biggest health problems in the world today. By segmenting the coronary arteries in medical images and examining them, important information about abnormal narrowing and plaque, which are main causes to these diseases, can be found. Manual segmentation of the coronary arteries are time consuming and dependent on the observer, which makes the need of automatic segmentation techniques apparent.

The aim of the thesis is to implement an accurate and time-efficient algorithm to segment coronary arteries in computed tomography (CT) images. To do this, a model based algorithm combined with a multiple hypothesis approach has been implemented. This was first done in 2D and tested on manually made phantoms.... (More)
Coronary heart diseases is one of the biggest health problems in the world today. By segmenting the coronary arteries in medical images and examining them, important information about abnormal narrowing and plaque, which are main causes to these diseases, can be found. Manual segmentation of the coronary arteries are time consuming and dependent on the observer, which makes the need of automatic segmentation techniques apparent.

The aim of the thesis is to implement an accurate and time-efficient algorithm to segment coronary arteries in computed tomography (CT) images. To do this, a model based algorithm combined with a multiple hypothesis approach has been implemented. This was first done in 2D and tested on manually made phantoms. Later on the algorithm was expanded to 3D, tested on phantoms and also on CT images of human hearts.

The thesis has been performed for the company Medviso. Medviso has created a software for cardiovascular image analysis, called Segment. All the implementation in this thesis has been preformed in Segment.

The algorithm was validated using two different datasets obtained from the Rotterdam Coronary Artery Evaluation Framework. Results from these show that the proposed method can be used to segment coronary arteries and that it, using only one user interaction, on average finds 64% of the sought for vessels with a tracking accuracy close to manual delineation. (Less)
Please use this url to cite or link to this publication:
author
Lemic, Anja LU
supervisor
organization
course
FMA820 20161
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3291-2016
ISSN
1404-6342
other publication id
2016:E13
language
English
id
8879728
date added to LUP
2016-08-25 13:44:52
date last changed
2016-08-25 13:44:52
@misc{8879728,
  abstract     = {Coronary heart diseases is one of the biggest health problems in the world today. By segmenting the coronary arteries in medical images and examining them, important information about abnormal narrowing and plaque, which are main causes to these diseases, can be found. Manual segmentation of the coronary arteries are time consuming and dependent on the observer, which makes the need of automatic segmentation techniques apparent.

The aim of the thesis is to implement an accurate and time-efficient algorithm to segment coronary arteries in computed tomography (CT) images. To do this, a model based algorithm combined with a multiple hypothesis approach has been implemented. This was first done in 2D and tested on manually made phantoms. Later on the algorithm was expanded to 3D, tested on phantoms and also on CT images of human hearts.

The thesis has been performed for the company Medviso. Medviso has created a software for cardiovascular image analysis, called Segment. All the implementation in this thesis has been preformed in Segment.

The algorithm was validated using two different datasets obtained from the Rotterdam Coronary Artery Evaluation Framework. Results from these show that the proposed method can be used to segment coronary arteries and that it, using only one user interaction, on average finds 64% of the sought for vessels with a tracking accuracy close to manual delineation.},
  author       = {Lemic, Anja},
  issn         = {1404-6342},
  language     = {eng},
  note         = {Student Paper},
  series       = {Master's Theses in Mathematical Sciences},
  title        = {Semi-Automatic Segmentation of Coronary Arteries in CT Images},
  year         = {2016},
}