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Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing
BACKGROUND: In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integratin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189096/ https://www.ncbi.nlm.nih.gov/pubmed/21871082 http://dx.doi.org/10.1186/1472-6947-11-54 |
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author | Hsieh, Thomas M Liu, Yi-Min Liao, Chun-Chih Xiao, Furen Chiang, I-Jen Wong, Jau-Min |
author_facet | Hsieh, Thomas M Liu, Yi-Min Liao, Chun-Chih Xiao, Furen Chiang, I-Jen Wong, Jau-Min |
author_sort | Hsieh, Thomas M |
collection | PubMed |
description | BACKGROUND: In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. METHODS: The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system. RESULTS: The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. CONCLUSIONS: Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use. |
format | Online Article Text |
id | pubmed-3189096 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31890962011-10-11 Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing Hsieh, Thomas M Liu, Yi-Min Liao, Chun-Chih Xiao, Furen Chiang, I-Jen Wong, Jau-Min BMC Med Inform Decis Mak Research Article BACKGROUND: In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. METHODS: The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system. RESULTS: The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. CONCLUSIONS: Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use. BioMed Central 2011-08-26 /pmc/articles/PMC3189096/ /pubmed/21871082 http://dx.doi.org/10.1186/1472-6947-11-54 Text en Copyright ©2011 Hsieh et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Hsieh, Thomas M Liu, Yi-Min Liao, Chun-Chih Xiao, Furen Chiang, I-Jen Wong, Jau-Min Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing |
title | Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing |
title_full | Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing |
title_fullStr | Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing |
title_full_unstemmed | Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing |
title_short | Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing |
title_sort | automatic segmentation of meningioma from non-contrasted brain mri integrating fuzzy clustering and region growing |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3189096/ https://www.ncbi.nlm.nih.gov/pubmed/21871082 http://dx.doi.org/10.1186/1472-6947-11-54 |
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