Clouds: A model for synergistic image segmentation

Image segmentation consists of recognizing the object in the image and precisely delineating its spatial extent. We present a model, called clouds, that exploits the synergism which commonly exists between recognition and delineation for more effective segmentation. The model can reduce user´s intervention to simple corrections or even eliminate it altogether, achieving high accuracy. We evaluate the method in the task of 3D MR image segmentation of the brain in isolating automatically: brain without medulla and spinal cord; just the cerebellum; and the brain hemispheres without medulla, spinal cord, and cerebellum. These structures are connected in several parts, which poses a serious challenge for simplistic segmentation strategies. The entire process takes a few seconds on modern PCs and provides accurate results. The applications for clouds go beyond medical imaging, opening new vistas in a variety of areas served by segmentation.