title: Active Contours Using Additive Local and Global Intensity Fitting Models for Intensity Inhomogeneous Image Segmentation.
abstract: This paper introduces an improved region based active contour method with a level set formulation. The proposed energy functional integrates both local and global intensity fitting terms in an additive formulation. Local intensity fitting term influences local force to pull the contour and confine it to object boundaries. In turn, the global intensity fitting term drives the movement of contour at a distance from the object boundaries. The global intensity term is based on the global division algorithm, which can better capture intensity information of an image than Chan-Vese (CV) model. Both local and global terms are mutually assimilated to construct an energy function based on a level set formulation to segment images with intensity inhomogeneity. Experimental results show that the proposed method performs better both qualitatively and quantitatively compared to other state-of-the-art-methods.
authors: Department of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
authors: Shafiullah
authors: S
authors: Soomro
authors: Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain.
authors: Farhan
authors: 0000-0003-4109-2645
authors: F
authors: Akram
authors: Korea Institute of Science and Technology Information, Daejeon 305-806, Republic of Korea.
authors: Jeong Heon
authors: JH
authors: Kim
authors: School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia.
authors: Toufique Ahmed
authors: TA
authors: Soomro
authors: Department of Computer Science and Engineering, Chung-Ang University, Seoul 156-756, Republic of Korea.
authors: Kwang Nam
authors: 0000-0002-7420-9216
authors: KN
authors: Choi
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging; D016000:Cluster Analysis; D003198:Computer Simulation; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results