Nonlinear diffusion, negentropy and restoration of mammographic images

Diffusive iterative filtering may be used to restore mammographic noisy images. Assuming that images are corrupted by additive gaussian noise, the negentropy as a statistical measure of the nongaussianity, can be used to estimate the amount of noise removed from noisy images. In particular, the evolution of the negentropy with the iterations is used to introduce a stopping criterion to end the diffusive filtering process. Because of computational complexity of the negentropy function, it is estimated by using an approximation of the negentropy introduced by Hyvärinen in the context of independent component analysis.