2 research outputs found
Scale-invariant segmentation of dynamic contrast-enhanced perfusion MR-images with inherent scale selection
Selection of the best set of scales is problematic when developing signaldriven
approaches for pixel-based image segmentation. Often, different
possibly conflicting criteria need to be fulfilled in order to obtain the best tradeoff
between uncertainty (variance) and location accuracy. The optimal set of
scales depends on several factors: the noise level present in the image material,
the prior distribution of the different types of segments, the class-conditional
distributions associated with each type of segment as well as the actual size of
the (connected) segments. We analyse, theoretically and through experiments,
the possibility of using the overall and class-conditional error rates as criteria
for selecting the optimal sampling of the linear and morphological scale spaces.
It is shown that the overall error rate is optimised by taking the prior class
distribution in the image material into account. However, a uniform (ignorant)
prior distribution ensures constant class-conditional error rates. Consequently,
we advocate for a uniform prior class distribution when an uncommitted, scaleinvariant
segmentation approach is desired.
Experiments with a neural net classifier developed for segmentation of
dynamic MR images, acquired with a paramagnetic tracer, support the
theoretical results. Furthermore, the experiments show that the addition of
spatial features to the classifier, extracted from the linear or morphological
scale spaces, improves the segmentation result compared to a signal-driven
approach based solely on the dynamic MR signal. The segmentation results
obtained from the two types of features are compared using two novel quality
measures that characterise spatial properties of labelled images