title: Fast and robust brain tumor segmentation using level set method with multiple image information.
abstract: Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation.
  
  
  
  To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images.
  
  
  
  In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index.
  
  
  
  Experimental results show improvement, in particular the increase of sensitivity at the same specificity, in segmenting all types of tumors except for the diffused tumor.
  
  
  
  The novel brain tumor segmentation method is clinical-oriented with fast, robust and accurate implementation and a minimal user interaction. The method effectively segmented homogeneously enhanced, non-enhanced, heterogeneously-enhanced, and ring-enhanced tumor under MR imaging. Though the method is limited by identifying edema and diffuse tumor, several possible solutions are suggested to turn the curve evolution into a fully functional clinical diagnosis tool.
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
authors.forename: Ka Hei
authors.initials: KH
authors.lastname: Lok
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.|Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
authors.forename: Lin
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Surgery, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
authors.forename: Xianlun
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.|Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
authors.forename: Defeng
authors.initials: D
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D001932:Brain Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D016011:Normal Distribution
keywords: Brain tumor segmentation; Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS); Singed Pressure Force (SPF); evaluation of tumor segmentation accuracy