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Conference Paper/Proceeding/Abstract 1073 views

Graph Based Lymphatic Vessel Wall Localisation and Tracking

Ehab Essa, Jonathan Jones, Xianghua Xie Orcid Logo

Graph-Based Representations in Pattern Recognition, Volume: 9069, Pages: 345 - 354

Swansea University Author: Xianghua Xie Orcid Logo

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DOI (Published version): 10.1007/978-3-319-18224-7_34

Abstract

We present a novel hidden Markov model (HMM) based approach to segment and track the lymph vessel in confocal microscopy images. The vessel borders are parameterised by radial basis functions (RBFs) so that the number of tracking points are reduced to a very few. The proposed method tracks the hidde...

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Published in: Graph-Based Representations in Pattern Recognition
Published: Springer 2015
Online Access: http://link.springer.com/chapter/10.1007%2F978-3-319-18224-7_34
URI: https://cronfa.swan.ac.uk/Record/cronfa22232
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spelling 2015-07-01T10:23:28.9856353 v2 22232 2015-07-01 Graph Based Lymphatic Vessel Wall Localisation and Tracking b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2015-07-01 SCS We present a novel hidden Markov model (HMM) based approach to segment and track the lymph vessel in confocal microscopy images. The vessel borders are parameterised by radial basis functions (RBFs) so that the number of tracking points are reduced to a very few. The proposed method tracks the hidden states that determine the border location along a set of normal lines obtained from the previous frame. The border observation is derived from edge-based features using steerable filters. Two Gaussian probability distributions for the vessel border and background are used to infer the emission probability. The transition probability is learnt by using the Baum-Welch algorithm. A new optimisation method for determining the best sequence of the hidden states is introduced. We transform the segmentation problem into a minimisation of s-excess graph cost. Each node in the graph corresponds to one state, and the weight for each node is defined using its emission probability. The inter-relation between neighbouring nodes is defined using the transition probability. Its optimal solution can be found in polynomial time using the s-t cut algorithm. Qualitative and quantitative analysis of the method on lymphatic vessel segmentation show superior performance of the proposed method compared to the traditional Viterbi algorithm. Conference Paper/Proceeding/Abstract Graph-Based Representations in Pattern Recognition 9069 345 354 Springer Graph cut, image segmentation, tracking, medical image analysis 31 5 2015 2015-05-31 10.1007/978-3-319-18224-7_34 http://link.springer.com/chapter/10.1007%2F978-3-319-18224-7_34 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2015-07-01T10:23:28.9856353 2015-07-01T10:05:56.7989459 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ehab Essa 1 Jonathan Jones 2 Xianghua Xie 0000-0002-2701-8660 3
title Graph Based Lymphatic Vessel Wall Localisation and Tracking
spellingShingle Graph Based Lymphatic Vessel Wall Localisation and Tracking
Xianghua Xie
title_short Graph Based Lymphatic Vessel Wall Localisation and Tracking
title_full Graph Based Lymphatic Vessel Wall Localisation and Tracking
title_fullStr Graph Based Lymphatic Vessel Wall Localisation and Tracking
title_full_unstemmed Graph Based Lymphatic Vessel Wall Localisation and Tracking
title_sort Graph Based Lymphatic Vessel Wall Localisation and Tracking
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Ehab Essa
Jonathan Jones
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title Graph-Based Representations in Pattern Recognition
container_volume 9069
container_start_page 345
publishDate 2015
institution Swansea University
doi_str_mv 10.1007/978-3-319-18224-7_34
publisher Springer
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
url http://link.springer.com/chapter/10.1007%2F978-3-319-18224-7_34
document_store_str 0
active_str 0
description We present a novel hidden Markov model (HMM) based approach to segment and track the lymph vessel in confocal microscopy images. The vessel borders are parameterised by radial basis functions (RBFs) so that the number of tracking points are reduced to a very few. The proposed method tracks the hidden states that determine the border location along a set of normal lines obtained from the previous frame. The border observation is derived from edge-based features using steerable filters. Two Gaussian probability distributions for the vessel border and background are used to infer the emission probability. The transition probability is learnt by using the Baum-Welch algorithm. A new optimisation method for determining the best sequence of the hidden states is introduced. We transform the segmentation problem into a minimisation of s-excess graph cost. Each node in the graph corresponds to one state, and the weight for each node is defined using its emission probability. The inter-relation between neighbouring nodes is defined using the transition probability. Its optimal solution can be found in polynomial time using the s-t cut algorithm. Qualitative and quantitative analysis of the method on lymphatic vessel segmentation show superior performance of the proposed method compared to the traditional Viterbi algorithm.
published_date 2015-05-31T03:26:28Z
_version_ 1763750957271744512
score 11.035634

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