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A Fully Automated Segmentation of Knee Bones and Cartilage Using Shape Context and Active Shape Models

Pirzamanbein, Behnaz (2012) MASM01 20121
Mathematical Statistics
Abstract (Swedish)
In this master's thesis a fully automated method is presented for seg-
menting bones and cartilage in magnetic resonance imaging (MRI) of the
knee. The knee joint is the most complex joint in the human body and
supports the weight of the whole body. This complexity and acute task of
the knee joint leads to a disabling disease called Osteoarthritis among the
adult population. The disease leads to loss of cartilage and torn cartilage
cannot be repaired unless surgical techniques are used. Therefore, one of
the important parts of nding the disease and planning the knee surgery
is to segment bones and cartilages in MRI.
The segmentation method is based on Statistical Shape Model (SSM)
and Active Shape Model (ASM) built from a MICCAI... (More)
In this master's thesis a fully automated method is presented for seg-
menting bones and cartilage in magnetic resonance imaging (MRI) of the
knee. The knee joint is the most complex joint in the human body and
supports the weight of the whole body. This complexity and acute task of
the knee joint leads to a disabling disease called Osteoarthritis among the
adult population. The disease leads to loss of cartilage and torn cartilage
cannot be repaired unless surgical techniques are used. Therefore, one of
the important parts of nding the disease and planning the knee surgery
is to segment bones and cartilages in MRI.
The segmentation method is based on Statistical Shape Model (SSM)
and Active Shape Model (ASM) built from a MICCAI 2010 Grand chal-
lenge training database. First, all the data are represented by points and
faces. A Shape context algorithm is applied on 60 data sets to obtain
consistent landmarks. The mentioned consistent landmarks and Princi-
pal Component Analysis are used to build a Statistical Shape Model. The
resulting model is used to automatically segment femur and tibia bones
and femur and tibia cartilages with Active Shape model. The algorithm is
tested on the remaining 40 MRI data sets provided by the Grand challenge
2010, and compared with six other submitted papers. (Less)
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author
Pirzamanbein, Behnaz
supervisor
organization
course
MASM01 20121
year
type
H2 - Master's Degree (Two Years)
subject
language
English
id
2968017
date added to LUP
2012-08-02 14:25:58
date last changed
2012-08-02 14:25:58
@misc{2968017,
  abstract     = {In this master's thesis a fully automated method is presented for seg-
menting bones and cartilage in magnetic resonance imaging (MRI) of the
knee. The knee joint is the most complex joint in the human body and
supports the weight of the whole body. This complexity and acute task of
the knee joint leads to a disabling disease called Osteoarthritis among the
adult population. The disease leads to loss of cartilage and torn cartilage
cannot be repaired unless surgical techniques are used. Therefore, one of
the important parts of nding the disease and planning the knee surgery
is to segment bones and cartilages in MRI.
The segmentation method is based on Statistical Shape Model (SSM)
and Active Shape Model (ASM) built from a MICCAI 2010 Grand chal-
lenge training database. First, all the data are represented by points and
faces. A Shape context algorithm is applied on 60 data sets to obtain
consistent landmarks. The mentioned consistent landmarks and Princi-
pal Component Analysis are used to build a Statistical Shape Model. The
resulting model is used to automatically segment femur and tibia bones
and femur and tibia cartilages with Active Shape model. The algorithm is
tested on the remaining 40 MRI data sets provided by the Grand challenge
2010, and compared with six other submitted papers.},
  author       = {Pirzamanbein, Behnaz},
  language     = {eng},
  note         = {Student Paper},
  title        = {A Fully Automated Segmentation of Knee Bones and Cartilage Using Shape Context and Active Shape Models},
  year         = {2012},
}