title: Level set segmentation of medical images based on local region statistics and maximum a posteriori probability.
abstract: This paper presents a variational level set method for simultaneous segmentation and bias field estimation of medical images with intensity inhomogeneity. In our model, the statistics of image intensities belonging to each different tissue in local regions are characterized by Gaussian distributions with different means and variances. According to maximum a posteriori probability (MAP) and Bayes' rule, we first derive a local objective function for image intensities in a neighborhood around each pixel. Then this local objective function is integrated with respect to the neighborhood center over the entire image domain to give a global criterion. In level set framework, this global criterion defines an energy in terms of the level set functions that represent a partition of the image domain and a bias field that accounts for the intensity inhomogeneity of the image. Therefore, image segmentation and bias field estimation are simultaneously achieved via a level set evolution process. Experimental results for synthetic and real images show desirable performances of our method.
authors.affiliation: School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China ; College of Science, China Three Gorges University, Yichang 443002, China.
authors.forename: Wenchao
authors.initials: W
authors.lastname: Cui
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.forename: Tao
authors.initials: T
authors.lastname: Lei
authors.forename: Yangyu
authors.initials: Y
authors.lastname: Fan
authors.forename: Yan
authors.initials: Y
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology; D003952:Diagnostic Imaging / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data; D014463:Ultrasonography / Q000706:statistics & numerical data