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Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation
The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174803/ https://www.ncbi.nlm.nih.gov/pubmed/30345308 http://dx.doi.org/10.1155/2018/8536854 |
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author | Jiang, Huiyan Li, Shaojie Li, Siqi |
author_facet | Jiang, Huiyan Li, Shaojie Li, Siqi |
author_sort | Jiang, Huiyan |
collection | PubMed |
description | The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver's bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works. |
format | Online Article Text |
id | pubmed-6174803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-61748032018-10-21 Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation Jiang, Huiyan Li, Shaojie Li, Siqi Biomed Res Int Research Article The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver's bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works. Hindawi 2018-09-24 /pmc/articles/PMC6174803/ /pubmed/30345308 http://dx.doi.org/10.1155/2018/8536854 Text en Copyright © 2018 Huiyan Jiang et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Jiang, Huiyan Li, Shaojie Li, Siqi Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation |
title | Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation |
title_full | Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation |
title_fullStr | Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation |
title_full_unstemmed | Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation |
title_short | Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation |
title_sort | registration-based organ positioning and joint segmentation method for liver and tumor segmentation |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174803/ https://www.ncbi.nlm.nih.gov/pubmed/30345308 http://dx.doi.org/10.1155/2018/8536854 |
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