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Typical Use

The Template Tool has been designed for use in automatically locating deformable shape in noisy (generally medical) data sets. Using it well requires some familiarity with the data and a little thought as to what is inteneded to be achieved. Start by determining the best number of points needed to accurately define the boundary of the object and whether an inner contour is also needed. Then experiment with the data to try to determine a reproducable way of positioning boundary points as accurately as possible. draw up guidelines for features to look out for and try to define the location of the boundary in a simple and reproducable manner. If necessary images can be pre-processed by the Imcalc Tool in order to assist with this process (eg: enhancing edges etc.).

Once you have a good idea of what you need to do then proceed with the following steps;

As the set of example datasets increases the posibility of building a global shape and grey-level model improves. This should only be done if the model has repeated difficulty with locating data. Eventually, (generally quite soon) you should converge on a model which gives reliable performance for this data set. Accuracies of better than a pixel are generally achievable at all points around the contour with some care even in noisy data. Significantly better performance than this is generally difficult to achieve due to the restrictions imposed by the eigen model and algorithms such as edge detection may be better if there is low noise and strong edge data is available. These techniques should not be considered as competetors among the solutions for boundary location. It is even acceptable to use templates in order to locate edges for more accurate measurement.

The posibilities for data pre-processing before building the deformable model are limitless, but an attempt should be made to work with data-sets which have the properties of uniform random errors (see the Imcalc Tool) as this is what is most consistent with the statistical assumptions behind greylevel modelling and least-squares based location algorithms.

The resulting tracking variables of orientation, scale and principle eigen-modes of the shape model make a good starting point as a reduced representation of the data for any subsequent anaysis such as statistical pattern recognition (eg: classification). However, these techniques are not expected to form the basis of a generic object recognition system as there is no robust way of automatically selecting or building models for arbitrary image data sets.


next up previous contents
Next: NMR-Segment Tool Up: SmartROI Tool Previous: SmartROI Parameters   Contents
root 2017-09-26