Convex relaxation for image segmentation by kernel mapping

This study proposes a novel multiregion image segmentation method using convex relaxation optimization and kernel mapping of the image data. The image data is transformed by a kernel function in order to support various image models while avoiding complex modeling. This is embedded implicitly in an objective function which is optimized by iterating a two-step strategy. First, a fixed point sequence is used to evaluate the regions parameters. Second, the image partition is updated by an efficient multiplier-based algorithm which uses the standard augmented Lagrangian method. A thorough experimental study is carried out over a multi-model synthetic dataset, the Berkeley database, as well as cardiac 3D data to show the effectiveness of the proposed method.