Such extensions could conceivably improve segmentation results in cases in which the search can be concentrated on only one section of an image. We obtained a number of interesting results while experimenting with our segmentation system. For example, we found that using different sorts of random distributions, such as Poisson and binomial, for the initial seed points make no appreciable difference in the final results. However, a set of manually distributed seed points with a high concentration of points near the edges of the lung will produce a higher quality outline in less iterations than will a random distribution of the same number of points. Figures 2 and 3 show a comparison of the intermediate steps and the final result of running the program in automatic mode for iterations on random and manual initial distributions, respectively.
Red line indicates outline of lung.
In addition, as might be expected, human interaction can greatly improve the results of the segmentation process. As shown in Figures 4 and 5, the results of four iterations with interactive reclassification for regions after each step tend to display a lower density of regions in difficult to segment areas than do those of a fully automatic run, since the user can force the program to ignore certain areas on which it might otherwise concentrate too much effort. Red line indicates outline of lung. Red line indicates outline of lung. Clearly, our region-based segmentation technique produces accurate results even without any user interaction.