title: Level set method with automatic selective local statistics for brain tumor segmentation in MR images.
abstract: The level set approach is a powerful tool for segmenting images. This paper proposes a method for segmenting brain tumor images from MR images. A new signed pressure function (SPF) that can efficiently stop the contours at weak or blurred edges is introduced. The local statistics of the different objects present in the MR images were calculated. Using local statistics, the tumor objects were identified among different objects. In this level set method, the calculation of the parameters is a challenging task. The calculations of different parameters for different types of images were automatic. The basic thresholding value was updated and adjusted automatically for different MR images. This thresholding value was used to calculate the different parameters in the proposed algorithm. The proposed algorithm was tested on the magnetic resonance images of the brain for tumor segmentation and its performance was evaluated visually and quantitatively. Numerical experiments on some brain tumor images highlighted the efficiency and robustness of this method.
authors.affiliation: Department of Information and Communication Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759, South Korea.
authors.forename: Kiran
authors.initials: K
authors.lastname: Thapaliya
authors.forename: Jae-Young
authors.initials: JY
authors.lastname: Pyun
authors.forename: Chun-Su
authors.initials: CS
authors.lastname: Park
authors.forename: Goo-Rak
authors.initials: GR
authors.lastname: Kwon
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001932:Brain Neoplasms / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Active contours; Chan–Vese model; Geodesic active contours; Image segmentation; Level set method; MR images