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Cardiac MRI segmentation with conditional random fields
Thesis (PhD)-- Stellenbosch University, 2013.
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| Format: | Thesis |
| Language: | en_ZA |
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Stellenbosch : Stellenbosch University
2013
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| _version_ | 1867614008712888321 |
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| access_status_str | Open Access |
| author | Dreijer, Janto Frederick |
| author2 | Herbst, B. M. |
| author_browse | Dreijer, Janto Frederick Herbst, B. M. |
| author_facet | Herbst, B. M. Dreijer, Janto Frederick |
| author_sort | Dreijer, Janto Frederick |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (PhD)-- Stellenbosch University, 2013. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/85847 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA |
| last_indexed | 2026-06-10T12:45:13.015Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/85847 Cardiac MRI segmentation with conditional random fields Dreijer, Janto Frederick Herbst, B. M. Du Preez, J. A. Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering . Magnetic resonance imaging Pattern recognition Segmentation Conditional Random Field Dissertations -- Electrical and electronic engineering Heart -- Magnetic resonance imaging Heart -- Imaging Heart -- Left ventricle -- Physiology Diagnostic imaging Theses -- Electrical and electronic engineering Thesis (PhD)-- Stellenbosch University, 2013. ENGLISH ABSTRACT: This dissertation considers automatic segmentation of the left cardiac ventricle in short axis magnetic resonance images. The presence of papillary muscles near the endocardium border makes simple threshold based segmentation difficult. The endo- and epicardium are modelled as two series of radii which are inter-related using features describing shape and motion. Image features are derived from edge information from human annotated images. The features are combined within a Conditional Random Field (CRF) – a discriminatively trained probabilistic model. Loopy belief propagation is used to infer segmentations when an unsegmented video sequence is given. Powell’s method is applied to find CRF parameters by minimising the difference between ground truth annotations and the inferred contours. We also describe how the endocardium centre points are calculated from a single human-provided centre point in the first frame, through minimisation of frame alignment error. We present and analyse the results of segmentation. The algorithm exhibits robustness against inclusion of the papillary muscles by integrating shape and motion information. Possible future improvements are identified. AFRIKAANSE OPSOMMING: Hierdie proefskrif bespreek die outomatiese segmentasie van die linkerhartkamer in kortas snit magnetiese resonansie beelde. Die teenwoordigheid van die papillêre spiere naby die endokardium grens maak eenvoudige drumpel gebaseerde segmentering moeilik. Die endo- en epikardium word gemodelleer as twee reekse van die radiusse wat beperk word deur eienskappe wat vorm en beweging beskryf. Beeld eienskappe word afgelei van die rand inligting van mens-geannoteerde beelde. Die funksies word gekombineer binne ’n CRF (Conditional Random Field) – ’n diskriminatief afgerigte waarskynlikheidsverdeling. “Loopy belief propagation” word gebruik om segmentasies af te lei wanneer ’n ongesegmenteerde video verskaf word. Powell se metode word toegepas om CRF parameters te vind deur die minimering van die verskil tussen mens geannoteerde segmentasies en die afgeleide kontoere. Ons beskryf ook hoe die endokardium se middelpunte bereken word vanaf ’n enkele mens-verskafte middelpunt in die eerste raam, deur die minimering van ’n raambelyningsfout. Ons analiseer die resultate van segmentering. Die algoritme vertoon robuustheid teen die insluiting van die papillêre spiere deur die integrasie van inligting oor die vorm en die beweging. Moontlike toekomstige verbeterings word geïdentifiseer. Doctoral 2013-11-21T07:52:09Z 2013-12-13T17:26:50Z 2013-11-21T07:52:09Z 2013-12-13T17:26:50Z 2013-12 Thesis http://hdl.handle.net/10019.1/85847 en_ZA Stellenbosch University xiv, 87 p. : ill. application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Magnetic resonance imaging Pattern recognition Segmentation Conditional Random Field Dissertations -- Electrical and electronic engineering Heart -- Magnetic resonance imaging Heart -- Imaging Heart -- Left ventricle -- Physiology Diagnostic imaging Theses -- Electrical and electronic engineering Dreijer, Janto Frederick Cardiac MRI segmentation with conditional random fields |
| title | Cardiac MRI segmentation with conditional random fields |
| title_full | Cardiac MRI segmentation with conditional random fields |
| title_fullStr | Cardiac MRI segmentation with conditional random fields |
| title_full_unstemmed | Cardiac MRI segmentation with conditional random fields |
| title_short | Cardiac MRI segmentation with conditional random fields |
| title_sort | cardiac mri segmentation with conditional random fields |
| topic | Magnetic resonance imaging Pattern recognition Segmentation Conditional Random Field Dissertations -- Electrical and electronic engineering Heart -- Magnetic resonance imaging Heart -- Imaging Heart -- Left ventricle -- Physiology Diagnostic imaging Theses -- Electrical and electronic engineering |
| url | http://hdl.handle.net/10019.1/85847 |
| work_keys_str_mv | AT dreijerjantofrederick cardiacmrisegmentationwithconditionalrandomfields |