document: 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 signal-driven approaches for pixel-based image segmentation . Often , different possibly conflicting criteria need to be fulfilled in order to obtain the best trade-off between uncertainty -LRB- variance -RRB- 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 -LRB- connected -RRB- 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 optimized by taking the prior class distribution in the image material into account . However , a uniform -LRB- ignorant -RRB- prior distribution ensures constant class-conditional error rates . Consequently , we advocate for a uniform prior class distribution when an uncommitted , scale-invariant segmentation approach is desired . Experiments with a neural net classifier developed for segmentation of dynamic magnetic resonance -LRB- MR -RRB- 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 characterize spatial properties of labelled images
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