Cargando…
Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method
BACKGROUND: Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshol...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472182/ https://www.ncbi.nlm.nih.gov/pubmed/26087652 http://dx.doi.org/10.1186/s12938-015-0055-z |
_version_ | 1782377016679464960 |
---|---|
author | Guo, Xiaoxi Huang, Shaohui Fu, Xiaozhu Wang, Boliang Huang, Xiaoyang |
author_facet | Guo, Xiaoxi Huang, Shaohui Fu, Xiaozhu Wang, Boliang Huang, Xiaoyang |
author_sort | Guo, Xiaoxi |
collection | PubMed |
description | BACKGROUND: Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation. METHODS: In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method. RESULTS: Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends. CONCLUSIONS: An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation. |
format | Online Article Text |
id | pubmed-4472182 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-44721822015-06-19 Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method Guo, Xiaoxi Huang, Shaohui Fu, Xiaozhu Wang, Boliang Huang, Xiaoyang Biomed Eng Online Research BACKGROUND: Fuzzy connectedness method has shown its effectiveness for fuzzy object extraction in recent years. However, two problems may occur when applying it to hepatic vessel segmentation task. One is the excessive computational cost, and the other is the difficulty of choosing a proper threshold value for final segmentation. METHODS: In this paper, an accelerated strategy based on a lookup table was presented first which can reduce the connectivity scene calculation time and achieve a speed-up factor of above 2. When the computing of the fuzzy connectedness relations is finished, a threshold is needed to generate the final result. Currently the threshold is preset by users. Since different thresholds may produce different outcomes, how to determine a proper threshold is crucial. According to our analysis of the hepatic vessel structure, a watershed-like method was used to find the optimal threshold. Meanwhile, by using Ostu algorithm to calculate the parameters for affinity relations and assigning the seed with the mean value, it is able to reduce the influence on the segmentation result caused by the location of the seed and enhance the robustness of fuzzy connectedness method. RESULTS: Experiments based on four different datasets demonstrate the efficiency of the lookup table strategy. These experiments also show that an adaptive threshold found by watershed-like method can always generate correct segmentation results of hepatic vessels. Comparing to a refined region-growing algorithm that has been widely used for hepatic vessel segmentation, fuzzy connectedness method has advantages in detecting vascular edge and generating more than one vessel system through the weak connectivity of the vessel ends. CONCLUSIONS: An improved algorithm based on fuzzy connectedness method is proposed. This algorithm has improved the performance of fuzzy connectedness method in hepatic vessel segmentation. BioMed Central 2015-06-19 /pmc/articles/PMC4472182/ /pubmed/26087652 http://dx.doi.org/10.1186/s12938-015-0055-z Text en © Guo et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Guo, Xiaoxi Huang, Shaohui Fu, Xiaozhu Wang, Boliang Huang, Xiaoyang Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method |
title | Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method |
title_full | Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method |
title_fullStr | Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method |
title_full_unstemmed | Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method |
title_short | Vascular segmentation in hepatic CT images using adaptive threshold fuzzy connectedness method |
title_sort | vascular segmentation in hepatic ct images using adaptive threshold fuzzy connectedness method |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4472182/ https://www.ncbi.nlm.nih.gov/pubmed/26087652 http://dx.doi.org/10.1186/s12938-015-0055-z |
work_keys_str_mv | AT guoxiaoxi vascularsegmentationinhepaticctimagesusingadaptivethresholdfuzzyconnectednessmethod AT huangshaohui vascularsegmentationinhepaticctimagesusingadaptivethresholdfuzzyconnectednessmethod AT fuxiaozhu vascularsegmentationinhepaticctimagesusingadaptivethresholdfuzzyconnectednessmethod AT wangboliang vascularsegmentationinhepaticctimagesusingadaptivethresholdfuzzyconnectednessmethod AT huangxiaoyang vascularsegmentationinhepaticctimagesusingadaptivethresholdfuzzyconnectednessmethod |