A deformable model for image segmentation in noisy medical images
Francisco L. Valverde, Nicolás Guil, J. Munoz, Qimin LI, Masahito Aoyama, K. Doi · 2002
Deformable-model-based segmentation techniques can overcome some limitations of the traditional image processing techniques. Currently developed deformable models can cope with gaps and another irregularities in object boundaries. However, they present problems in noisy images. Our approach is able to segment objects in noisy images by defining a new energy function associated with image noise and avoiding the tendency of contour points to bunch up. The model is validated for vessel segmentation on mammograms.