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Combinatorial optimisation for arterial image segmentation. / Ehab Mohamed Mahmoud Essa

Swansea University Author: Ehab Mohamed Mahmoud Essa

Abstract

Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This t...

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Published: 2014
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
URI: https://cronfa.swan.ac.uk/Record/cronfa42617
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last_indexed 2018-08-03T10:10:37Z
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spelling 2018-08-02T16:24:29.8682073 v2 42617 2018-08-02 Combinatorial optimisation for arterial image segmentation. 1e2e134b148109090dadd3c27585e0d5 NULL Ehab Mohamed Mahmoud Essa Ehab Mohamed Mahmoud Essa true true 2018-08-02 Cardiovascular disease is one of the leading causes of the mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods. E-Thesis Computer science.;Medical imaging. 31 12 2014 2014-12-31 COLLEGE NANME Computer Science COLLEGE CODE Swansea University Doctoral Ph.D 2018-08-02T16:24:29.8682073 2018-08-02T16:24:29.8682073 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ehab Mohamed Mahmoud Essa NULL 1 0042617-02082018162508.pdf 10805375.pdf 2018-08-02T16:25:08.5730000 Output 11408456 application/pdf E-Thesis true 2018-08-02T16:25:08.5730000 false
title Combinatorial optimisation for arterial image segmentation.
spellingShingle Combinatorial optimisation for arterial image segmentation.
Ehab Mohamed Mahmoud Essa
title_short Combinatorial optimisation for arterial image segmentation.
title_full Combinatorial optimisation for arterial image segmentation.
title_fullStr Combinatorial optimisation for arterial image segmentation.
title_full_unstemmed Combinatorial optimisation for arterial image segmentation.
title_sort Combinatorial optimisation for arterial image segmentation.
author_id_str_mv 1e2e134b148109090dadd3c27585e0d5
author_id_fullname_str_mv 1e2e134b148109090dadd3c27585e0d5_***_Ehab Mohamed Mahmoud Essa
author Ehab Mohamed Mahmoud Essa
author2 Ehab Mohamed Mahmoud Essa
format E-Thesis
publishDate 2014
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 mortality in the western world. Many imaging modalities have been used to diagnose cardiovascular diseases. However, each has different forms of noise and artifacts that make the medical image analysis field important and challenging. This thesis is concerned with developing fully automatic segmentation methods for cross-sectional coronary arterial imaging in particular, intra-vascular ultrasound and optical coherence tomography, by incorporating prior and tracking information without any user intervention, to effectively overcome various image artifacts and occlusions. Combinatorial optimisation methods are proposed to solve the segmentation problem in polynomial time. A node-weighted directed graph is constructed so that the vessel border delineation is considered as computing a minimum closed set. A set of complementary edge and texture features is extracted. Single and double interface segmentation methods are introduced. Novel optimisation of the boundary energy function is proposed based on a supervised classification method. Shape prior model is incorporated into the segmentation framework based on global and local information through the energy function design and graph construction. A combination of cross-sectional segmentation and longitudinal tracking is proposed using the Kalman filter and the hidden Markov model. The border is parameterised using the radial basis functions. The Kalman filter is used to adapt the inter-frame constraints between every two consecutive frames to obtain coherent temporal segmentation. An HMM-based border tracking method is also proposed in which the emission probability is derived from both the classification-based cost function and the shape prior model. The optimal sequence of the hidden states is computed using the Viterbi algorithm. Both qualitative and quantitative results on thousands of images show superior performance of the proposed methods compared to a number of state-of-the-art segmentation methods.
published_date 2014-12-31T03:53:19Z
_version_ 1763752646304333824
score 11.014067

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