Effective Level Set Image Segmentation With a Kernel Induced Data Term

Abstract—This study investigates level set multiphase image segmentation by kernel mapping and piecewise constant modeling of the image data thereof. A kernel function maps implicitly the original data into data of a higher dimension so that the piecewise constant model becomes applicable. This leads to a flexible and effective alternative to complex modeling of the image data. The method uses an active curve objective functional with two terms: an original term which evaluates the deviation of the mapped image data within each segmentation region from the piecewise constant model and a classic length regularization term for smooth region boundaries. Functional minimization is carried out by iterations of two consecutive steps: 1) minimization with respect to the segmentation by curve evolution via Euler-Lagrange descent equations and 2) minimization with respect to the regions parameters via fixed point iterations. Using a common kernel function, this step amounts to a mean shift ...

• Type: Journal
• Year: 2010
• Where: TIP
• Authors: Mohamed Ben Salah, Amar Mitiche, Ismail Ben Ayed
• Keywords: Image Data, Images, Piecewise Constant Model, TIP 2010