Variational segmentation model for images with intensity inhomogeneity and Poisson noise
In this paper, we propose a variational segmentation model to deal with intensity inhomogeneity and Poisson noise. An energy functional is first proposed, which uses a data-fidelity term deduced from Poisson distribution instead of the usual L 2 norm as a measure of fidelity. Due to the new data-fidelity measure, this energy functional can fit the image intensity more accurately while it can diminish the influence of Poisson noise on segmentation results. We then reformulate the energy function as globally convex formulation, which allows for more flexible initialization. The final convex energy functional is minimized via the dual formulation instead of the usually used gradient descent method. Experimental results show that the proposed model can efficiently segment images with intensity inhomogeneity and Poisson noise.
intensity inhomogeneity poisson noise image segmentation variational method