  We consider the problem of detecting anomalies in the directional
distribution of fibre materials observed in 3D images. We divide the image into
a set of scanning windows and classify them into two clusters: homogeneous
material and anomaly. Based on a sample of estimated local fibre directions,
for each scanning window we compute several classification attributes, namely
the coordinate wise means of local fibre directions, the entropy of the
directional distribution, and a combination of them. We also propose a new
spatial modification of the Stochastic Approximation Expectation-Maximization
(SAEM) algorithm. Besides the clustering we also consider testing the
significance of anomalies. To this end, we apply a change point technique for
random fields and derive the exact inequalities for tail probabilities of a
test statistics. The proposed methodology is first validated on simulated
images. Finally, it is applied to a 3D image of a fibre reinforced polymer.
