title: Model-driven, probabilistic level set based segmentation of magnetic resonance images of the brain.
abstract: Accurate segmentation of magnetic resonance (MR) images of the brain to differentiate features such as soft tissue, tumor, edema and necrosis is critical for both diagnosis and treatment purposes. Region-based formulations of geometric active contour models are popular choices for segmentation of MR and other medical images. Most of the traditional region-based formulations model local region intensity by assuming a piecewise constant approximation. However, the piecewise constant approximation rarely holds true for medical images such as MR images due to the presence of noise and bias field, which invariably results in a poor segmentation of the image. To overcome this problem, we have developed a probabilistic region-based active contour model for automatic segmentation of MR images of the brain. In our approach, a mixture of Gaussian distributions is used to accurately model the arbitrarily shaped local region intensity distribution. Prior spatial information derived from probabilistic atlases is also integrated into the level set evolution framework for guiding the segmentation process. Our experiments with a series of publicly available brain MR images show that the proposed active contour model gives stable and accurate segmentation results when compared to the traditional region based formulations.
authors.affiliation: Deptof Biomedical Engineering, The University of Texas, Austin, TX 78712, USA. fvnishant@mail.utexas.edu
authors.forename: Nishant
authors.initials: N
authors.lastname: Verma
authors.forename: Gautam S
authors.initials: GS
authors.lastname: Muralidhar
authors.forename: Alan C
authors.initials: AC
authors.lastname: Bovik
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Cowperthwaite
authors.forename: Mia K
authors.initials: MK
authors.lastname: Markey
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D011336:Probability