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2D and 3D segmentation of medical images. / Jonathan-Lee Jones

Swansea University Author: Jonathan-Lee Jones

Abstract

"Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different for...

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Published: 2015
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42504
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first_indexed 2018-08-02T18:54:52Z
last_indexed 2018-08-03T10:10:19Z
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spelling 2018-08-02T16:24:29.4781967 v2 42504 2018-08-02 2D and 3D segmentation of medical images. f0656fa2f068fde3d989474992db28b4 NULL Jonathan-Lee Jones Jonathan-Lee Jones true true 2018-08-02 "Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve." E-Thesis Medical imaging.;Computer science. 31 12 2015 2015-12-31 COLLEGE NANME Computer Science COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:29.4781967 2018-08-02T16:24:29.4781967 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jonathan-Lee Jones NULL 1 0042504-02082018162459.pdf 10801734.pdf 2018-08-02T16:24:59.7100000 Output 18008360 application/pdf E-Thesis true 2018-08-02T16:24:59.7100000 false
title 2D and 3D segmentation of medical images.
spellingShingle 2D and 3D segmentation of medical images.
Jonathan-Lee Jones
title_short 2D and 3D segmentation of medical images.
title_full 2D and 3D segmentation of medical images.
title_fullStr 2D and 3D segmentation of medical images.
title_full_unstemmed 2D and 3D segmentation of medical images.
title_sort 2D and 3D segmentation of medical images.
author_id_str_mv f0656fa2f068fde3d989474992db28b4
author_id_fullname_str_mv f0656fa2f068fde3d989474992db28b4_***_Jonathan-Lee Jones
author Jonathan-Lee Jones
author2 Jonathan-Lee Jones
format E-Thesis
publishDate 2015
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description "Cardiovascular disease is one of the leading causes of the morbidity and mortality in the western world today. Many different imaging modalities are in place today to diagnose and investigate cardiovascular diseases. Each of these, however, has strengths and weaknesses. There are different forms of noise and artifacts in each image modality that combine to make the field of medical image analysis both important and challenging. The aim of this thesis is develop a reliable method for segmentation of vessel structures in medical imaging, combining the expert knowledge of the user in such a way as to maintain efficiency whilst overcoming the inherent noise and artifacts present in the images. We present results from 2D segmentation techniques using different methodologies, before developing 3D techniques for segmenting vessel shape from a series of images. The main drive of the work involves the investigation of medical images obtained using catheter based techniques, namely Intra Vascular Ultrasound (IVUS) and Optical Coherence Tomography (OCT). We will present a robust segmentation paradigm, combining both edge and region information to segment the media-adventitia, and lumenal borders in those modalities respectively. By using a semi-interactive method that utilizes "soft" constraints, allowing imprecise user input which provides a balance between using the user's expert knowledge and efficiency. In the later part of the work, we develop automatic methods for segmenting the walls of lymph vessels. These methods are employed on sequential images in order to obtain data to reconstruct the vessel walls in the region of the lymph valves. We investigated methods to segment the vessel walls both individually and simultaneously, and compared the results both quantitatively and qualitatively in order obtain the most appropriate for the 3D reconstruction of the vessel wall. Lastly, we adapt the semi-interactive method used on vessels earlier into 3D to help segment out the lymph valve. This involved the user interactive method to provide guidance to help segment the boundary of the lymph vessel, then we apply a minimal surface segmentation methodology to provide segmentation of the valve."
published_date 2015-12-31T03:53:06Z
_version_ 1763752632407556096
score 11.016258

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