title: A partition-based active contour model incorporating local information for image segmentation.
abstract: Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.
authors.affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Jiao
authors.identifier: 0000-0002-4630-5307
authors.initials: J
authors.lastname: Shi
authors.affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Jiaji
authors.initials: J
authors.lastname: Wu
authors.affiliation: School of Computer Science Engineering, Kyungpook National University, Daegu 702-701, Republic of Korea.
authors.forename: Anand
authors.identifier: 0000-0002-0737-2021
authors.initials: A
authors.lastname: Paul
authors.affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Licheng
authors.initials: L
authors.lastname: Jiao
authors.affiliation: Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Institute of Intelligent Information Processing, Xidian University, Xi'an, Shaanxi 710071, China.
authors.forename: Maoguo
authors.initials: M
authors.lastname: Gong
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical