Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing

Thomas M. Hsieh, Yi Min Liu, Chun Chih Liao, Furen Xiao, I. Jen Chiang, Jau Min Wong

研究成果: 雜誌貢獻文章

51 引文 (Scopus)

摘要

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.

原文英語
文章編號54
期刊BMC Medical Informatics and Decision Making
11
發行號1
DOIs
出版狀態已發佈 - 2011
對外發佈Yes

指紋

Meningioma
Cluster Analysis
Magnetic Resonance Imaging
Brain
Neoplasms
Brain Neoplasms
Brain Edema
Information Systems
Pathology
Physicians

ASJC Scopus subject areas

  • Health Informatics
  • Health Policy

引用此文

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.

於: BMC Medical Informatics and Decision Making, 卷 11, 編號 1, 54, 2011.

研究成果: 雜誌貢獻文章

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. 於: BMC Medical Informatics and Decision Making. 2011 ; 卷 11, 編號 1.
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abstract = "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.",
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