  The paper considers the problem of anomaly detection in 3D images of fibre
materials. The spatial Stochastic Expectation Maximisation algorithm and
Adaptive Weights Clustering are applied to solve this problem. The initial 3D
grey scale image was divided into small cubes subject to clustering. For each
cube clustering attributes values were calculated: mean local direction and
directional entropy. Clustering is conducted according to the given attributes.
The proposed methods are tested on the simulated images and on real fibre
materials.
