The Computed Tomography (CT) modality shows not only the body of the patient in the volumes it generates, but also the clothing, the cushion and the table. This might be a problem especially for two applications. The first is 3D visualization, where the table has high density parts that might hide regions of interest. The second is registration of acquisitions obtained at different time points; indeed, the table and cushions might be visible in one data set only, and their positions and shapes may vary, making the registration less accurate. An automatic approach for extracting the body would solve those problems. It should be robust, reliable, and fast. We therefore propose a multi-scale method based on deformable models. The idea is to move a surface across the image that attaches to the boundaries of the body. We iteratively compute forces which take into account local information around the surface. Those make it move through the table but ensure that it stops when coming close to the body. Our model has elastic properties; moreover, we take into account the fact that some regions in the volume convey more information than others by giving them more weight. This is done by using normalized convolution when regularizing the surface. The algorithm*, tested on a database of over a hundred volumes of
whole body, chest or lower abdomen, has proven to be very efficient, even for volumes with up to 900 slices, providing accurate results in an average time of 6 seconds. It is also robust against noise and variations of scale and table's shape.