Novel level set based statistical frameworks for segmentation of MR images
In summary, two automated frameworks for segmentation of medical images are proposed. They are the joint curve evolution and the EM-joint shape based algorithms. In addition, the generalization of our frameworks to 3D models of multiple struc- tures. The resulting implementation is tested on a varie...
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sg-ntu-dr.10356-507262023-07-04T17:09:18Z Novel level set based statistical frameworks for segmentation of MR images Mahshid Farzinfar Teoh Eam Khwang School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems In summary, two automated frameworks for segmentation of medical images are proposed. They are the joint curve evolution and the EM-joint shape based algorithms. In addition, the generalization of our frameworks to 3D models of multiple struc- tures. The resulting implementation is tested on a variety of studies to parceling of anatomical structures. The proposed algorithms can be used for surgery navigation and 3D visualization. They could also be applied to neuroscience studies in finding new diseases related to anatomical characteristics and the increase in reliability in diagnosing of illnesses such as schizophrenia. DOCTOR OF PHILOSOPHY (EEE) 2012-09-24T05:42:51Z 2012-09-24T05:42:51Z 2012 2012 Thesis Mahshid, F. (2012). Novel level set based statistical frameworks for segmentation of MR images. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/50726 10.32657/10356/50726 en 202 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Mahshid Farzinfar Novel level set based statistical frameworks for segmentation of MR images |
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In summary, two automated frameworks for segmentation of medical images are proposed. They are the joint curve evolution and the EM-joint shape based algorithms. In addition, the generalization of our frameworks to 3D models of multiple struc- tures. The resulting implementation is tested on a variety of studies to parceling of anatomical structures. The proposed algorithms can be used for surgery navigation and 3D visualization. They could also be applied to neuroscience studies in finding new diseases related to anatomical characteristics and the increase in reliability in diagnosing of illnesses such as schizophrenia. |
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Teoh Eam Khwang |
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Teoh Eam Khwang Mahshid Farzinfar |
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Theses and Dissertations |
author |
Mahshid Farzinfar |
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Mahshid Farzinfar |
title |
Novel level set based statistical frameworks for segmentation of MR images |
title_short |
Novel level set based statistical frameworks for segmentation of MR images |
title_full |
Novel level set based statistical frameworks for segmentation of MR images |
title_fullStr |
Novel level set based statistical frameworks for segmentation of MR images |
title_full_unstemmed |
Novel level set based statistical frameworks for segmentation of MR images |
title_sort |
novel level set based statistical frameworks for segmentation of mr images |
publishDate |
2012 |
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https://hdl.handle.net/10356/50726 |
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1772829072036986880 |