Automatic parameter setting for balloon models

We describe a 'learning-from-examples'-method to automatically adjust parameters for a balloon model. Our goal is to segment arbitrarily shaped objects in medical images with as little human interaction as possible. For our model, we identified six significant parameters that are adjusted with respect to certain applications. These parameters are computed from one manual segmentation drawn by a physician. (1) The maximal edge length is derived from a polygon-approximation of the manual segmentation. (2) The size of the image subset that exerts external influences on edges is set according to the scale of gradients normal to the contour. (3) The offset of the assignment from graylevels to image potentials is adjusted such that the propulsive pressure overcomes image potentials in homogeneous parts of the image. (4) The gain of this assignment is tuned to stop the contour at the border of objects of interest. (5) The strength of deformation force is computed to balance the contour at edges with ambiguous image information. (6) These parameters are computed for both, positive and negative pressure. The variation that gives the best segmentation result is chosen. The analytically derived adjustments are optimized with a genetic algorithm that evolutionarily reduces the number of misdetected pixels. The method is used on a series of histochemically stained cells. Similar segmentation quality is obtained applying both, manual and automatic parameter setting. We further use the method on laryngoscopic color image sequences, where, even for experts, the manual adjustment of parameters is not applicable.