title: [A graph cuts-based interactive method for segmentation of magnetic resonance images of meningioma].
abstract: For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. lishuanqiang@gmail.com
authors.forename: Shuan-qiang
authors.initials: SQ
authors.lastname: Li
authors.forename: Qian-jin
authors.initials: QJ
authors.lastname: Feng
authors.forename: Wu-fan
authors.initials: WF
authors.lastname: Chen
authors.forename: Ya-zhong
authors.initials: YZ
authors.lastname: Lin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008577:Meningeal Neoplasms / Q000175:diagnosis* / Q000473:pathology; D008579:Meningioma / Q000175:diagnosis* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*