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160of subsampling would have to be used for all but the smallest images.Once the automatic unsupervised segmentation is accomplished using themethods presented here, other methods which make use of prior knowledge couldbe applied. My method would then play the role of reducing noise, which wouldassist shape-based feature finding methods, such as deformable templates. Forexample, in the dog lung example (section 5.3.4), a shape-based method couldstart from an initial estimate of the lung boundary given by the union of the twobrightest segments. An alternate approach would be to find a correlation peak betweenthe image and a template, which can be done very quickly with the Fouriertransform; a discussion of the Fourier transform can be found in most elementaryengineering texts, such as Oppenheim et. al. (1983).Shape-based methods would likely require extraction of the spatially connectedcomponents in the final segmentation; each connected component could then becompared to predefined templates (perhaps with parameters for rotation and scaling)to determine the presence or absence of features of interest in the image.If training data are available, the results of this segmentation method could beused to automatically search an image database for features of interest. The segmentationwould be performed on the training data first, perhaps with interactivechoice of the number of segments. Characteristics of the feature of interest wouldbe noted; these could include model parameters, such as mean and variance of aparticular component in the Gaussian mixture, or nonmodel observations, such asthe size and shape of one or more connected components representing the featureof interest. Then, new data would be segmented in an automatic mode. The modelparameters of the resulting segmentation could be compared to the training dataresults; connected components could also be extracted for analysis and comparisonto training data.With sufficient training data, one could fit a CART or logistic regression model