title: [Segmental active contour model combining regional information].
abstract: A segmental active contour model integrating region information is proposed. Different deformation schemes are used at two stages for segmenting the object correctly in image plane. At the first stage the contour of the model is divided hierarchically into several segments, each of which deforms respectively using affine transformation. After the contour deforms to the approximate boundary of object, a fine matching method using statistical information of local region to redefine the external energy of the model is used to make the contour fit the object's boundary exactly. The algorithm is effective, as the reformative approaches of computing the internal energy and external energy are proposed to reduce the algorithm complexity. The experimental results indicate that the proposed model is robust to local minima and able to search for concave objects.
authors.affiliation: Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Ran
authors.forename: Feihu
authors.initials: F
authors.lastname: Qi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods