title: Unsupervised multiphase segmentation: a phase balancing model.
abstract: Variational models have been studied for image segmentation application since the Mumford-Shah functional was introduced in the late 1980s. In this paper, we focus on multiphase segmentation with a new regularization term that yields an unsupervised segmentation model. We propose a functional that automatically chooses a favorable number of phases as it segments the image. The primary driving force of the segmentation is the intensity fitting term while a phase scale measure complements the regularization term. We propose a fast, yet simple, brute-force numerical algorithm and present experimental results showing the robustness and stability of the proposed model.
authors.affiliation: Adel Research, Inc., Los Angeles, CA, USA. berta.sandberg@adelresearch.com
authors.forename: Berta
authors.initials: B
authors.lastname: Sandberg
authors.forename: Sung Ha
authors.initials: SH
authors.lastname: Kang
authors.forename: Tony F
authors.initials: TF
authors.lastname: Chan
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008962:Models, Theoretical