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The purpose of the presented work is the automatic localization of the heart from 4D multi-slice magnetic resonance images (MRI). Well known active contour extraction techniques such as 'snakes' or 'balloons' require precise initialization which is mostly done interactively by the user in existing systems. A new method for the automatic initialization of such models is presented here for application on 4D MRI dataset acquired from the human heart. The method consists of two main steps: a global localization of the heart and a coarse initialization of the contours. Furthermore, it is shown how this initialization can be used for an automatic fine segmentation by an active contour model. The temporal analysis of the heart beat cycle is well suited for localization purposes. A 'temporal variance image' is thus first computed at each spatial slice location. These variance images consistently highlight the heart due to its wall movement and the heavy blood flow. By thresholding the variance images, projecting them into a single image, thresholding again and selecting the largest resulting object, a 'binary confidence mask' is computed for the heart region. This mask allow us to extract one binary image of the heart for each spatial slice location, regardless of temporal location. In the initialization stage, an 'initial contour' is matched to each of these masked images by affine transform, adapting size, location, aspect ratio and orientation. Initial contours may be acquired from a predefined model. In absence of such a model, ellipses were successfully applied as generic initial contours. For this stage, 2D contours were used; however, extensions to 3D are straightforward. The affine adapted contours are then considered as initialization for a multi- step active contour model for the accurate extraction of the heart walls: the contours are deformed according to the masked binary images, further refined on the temporal mean images for each spatial slice location, and finally the outer heart walls are tracked over time on the actual images at each spatial slice location. This approach, making use of existing active contour models yields an efficient and robust method for an exact extraction of the heart contours.
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