title: Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing.
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.
authors.affiliation: Institute of Biomedical Engineering, and College of Medicine, National Taiwan University, Taipei.
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Hsieh
authors.forename: Yi-Min
authors.initials: YM
authors.lastname: Liu
authors.forename: Chun-Chih
authors.initials: CC
authors.lastname: Liao
authors.forename: Furen
authors.initials: F
authors.lastname: Xiao
authors.forename: I-Jen
authors.initials: IJ
authors.lastname: Chiang
authors.forename: Jau-Min
authors.initials: JM
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D016000:Cluster Analysis; D017143:Fuzzy Logic; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D008579:Meningioma / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods