  Intracranial hemorrhage occurs when blood vessels rupture or leak within the
brain tissue or elsewhere inside the skull. It can be caused by physical trauma
or by various medical conditions and in many cases leads to death. The
treatment must be started as soon as possible, and therefore the hemorrhage
should be diagnosed accurately and quickly. The diagnosis is usually performed
by a radiologist who analyses a Computed Tomography (CT) scan containing a
large number of cross-sectional images throughout the brain. Analysing each
image manually can be very time-consuming, but automated techniques can help
speed up the process. While much of the recent research has focused on solving
this problem by using supervised machine learning algorithms,
publicly-available training data remains scarce due to privacy concerns. This
problem can be alleviated by unsupervised algorithms. In this paper, we propose
a fully-unsupervised algorithm which is based on the mixture models. Our
algorithm utilizes the fact that the properties of hemorrhage and healthy
tissues follow different distributions, and therefore an appropriate
formulation of these distributions allows us to separate them through an
Expectation-Maximization process. In addition, our algorithm is able to
adaptively determine the number of clusters such that all the hemorrhage
regions can be found without including noisy voxels. We demonstrate the results
of our algorithm on publicly-available datasets that contain all different
hemorrhage types in various sizes and intensities, and our results are compared
to earlier unsupervised and supervised algorithms. The results show that our
algorithm can outperform the other algorithms with most hemorrhage types.
