Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information
This work addresses image segmentation challenges for applications in medical imaging or computer vision, but it appears incremental as it builds on existing split Bregman and mesh adaptation techniques.
The authors tackled the problem of image segmentation in complex, noisy images by developing a finite element-based method with anisotropic mesh adaptation, which achieved sharp interface detection and robustness against various types of noise.
A finite element-based image segmentation strategy enhanced by an anisotropic mesh adaptation procedure is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation. More precisely, a Bayesian energy functional is considered to account for image spatial information, ensuring that the methodology is able to identify inhomogeneous spatial patterns in complex images. In addition, the anisotropic mesh adaptation guarantees a sharp detection of the interface between background and foreground of the image, with a reduced number of degrees of freedom. The resulting split-adapt Bregman algorithm is tested on a set of real images showing the accuracy and robustness of the method, even in the presence of Gaussian, salt and pepper and speckle noise.