Segmentation of magnetic resonance images for diagnostic applications.

Osteoarthritis (OA) and Bone Marrow Edema (BME) are very common diseases of the knee. They are characterised by the thinning of the joint cartilage and instances of abnormal bone respectively. Magnetic Resonance Imaging (MRI) has been increasing popular in characterization of the knee as it allows f...

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Main Author: Leow, Jiamin.
Other Authors: Poh Chueh Loo
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/45431
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-454312023-03-03T15:35:17Z Segmentation of magnetic resonance images for diagnostic applications. Leow, Jiamin. Poh Chueh Loo School of Chemical and Biomedical Engineering DRNTU::Science::Medicine::Optical instruments Osteoarthritis (OA) and Bone Marrow Edema (BME) are very common diseases of the knee. They are characterised by the thinning of the joint cartilage and instances of abnormal bone respectively. Magnetic Resonance Imaging (MRI) has been increasing popular in characterization of the knee as it allows for excellent soft tissue contrast and high-spatial resolution. Image segmentation is critical when analysing these MRI images in order to quantitatively determine the condition of the various components of the knee. In this project, we developed and evaluated a fully automatic image segmentation method for application to MRI Isotropic and Non-isotropic, PD-weighted and T2-FSw images of the knee joint. The structures of interest were the Femur, Femoral Cartilage, Tibia, Patella and Patella Cartilage. Two types of Deformable Registrations in Insight Toolkit (ITK) were used. This first was B-Spline Registration and the second was Free-Form Deformation (FFD) Registration. Mutual Information (MI) was used as similarity measure metric and Dice Similarity Coefficient (DSC) was used as an evaluation measure of the quality of the registration. Bachelor of Engineering (Chemical and Biomolecular Engineering) 2011-06-13T07:59:25Z 2011-06-13T07:59:25Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/45431 en Nanyang Technological University 71 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Medicine::Optical instruments
spellingShingle DRNTU::Science::Medicine::Optical instruments
Leow, Jiamin.
Segmentation of magnetic resonance images for diagnostic applications.
description Osteoarthritis (OA) and Bone Marrow Edema (BME) are very common diseases of the knee. They are characterised by the thinning of the joint cartilage and instances of abnormal bone respectively. Magnetic Resonance Imaging (MRI) has been increasing popular in characterization of the knee as it allows for excellent soft tissue contrast and high-spatial resolution. Image segmentation is critical when analysing these MRI images in order to quantitatively determine the condition of the various components of the knee. In this project, we developed and evaluated a fully automatic image segmentation method for application to MRI Isotropic and Non-isotropic, PD-weighted and T2-FSw images of the knee joint. The structures of interest were the Femur, Femoral Cartilage, Tibia, Patella and Patella Cartilage. Two types of Deformable Registrations in Insight Toolkit (ITK) were used. This first was B-Spline Registration and the second was Free-Form Deformation (FFD) Registration. Mutual Information (MI) was used as similarity measure metric and Dice Similarity Coefficient (DSC) was used as an evaluation measure of the quality of the registration.
author2 Poh Chueh Loo
author_facet Poh Chueh Loo
Leow, Jiamin.
format Final Year Project
author Leow, Jiamin.
author_sort Leow, Jiamin.
title Segmentation of magnetic resonance images for diagnostic applications.
title_short Segmentation of magnetic resonance images for diagnostic applications.
title_full Segmentation of magnetic resonance images for diagnostic applications.
title_fullStr Segmentation of magnetic resonance images for diagnostic applications.
title_full_unstemmed Segmentation of magnetic resonance images for diagnostic applications.
title_sort segmentation of magnetic resonance images for diagnostic applications.
publishDate 2011
url http://hdl.handle.net/10356/45431
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