When the similarity test fails, the growing process stops and the target is segmented from the rest of the image. One difficulty with region growing is in defining the similarity measure. Its accuracy determines the accuracy of the segmentation, whether the target grows too big or too small. Several similarity measures have been tried in the past, with varying degrees of success. CMRI has taken the approach of determining a similarity measure empirically, rather than analytically, which is where the neural networks are employed.
Neural networks are methods of pattern classification that are nonlinear and empirical.
The neural network determines a similarity measure based on data presented to it-it learns by example. In this case, the examples are a collection of 22 features derived from the CT images, each of which contains an explosive. Features are extracted from explosive and nonexplosive objects, giving the neural network examples of both. Over repeated training sessions the network eventually learns to distinguish between features from explosives and those from nonexplosives. This enables it to make the same distinction when presented with new image data.