Knowledge-based vascular segmentation methods typically rely on a pre-built training set of segmented images,
which is used to estimate the probability of each voxel to belong to a particular tissue. In 3D Rotational Angiography
(3DRA) the same tissue can correspond to different intensity ranges depending on the imaging device,
settings and contrast injection protocol. As a result, pre-built training sets do not apply to all images and
the best segmentation results are often obtained when the training set is built specifically for each individual
image. We present an Image Intensity Standardization (IIS) method designed to ensure a correspondence between
specific tissues and intensity ranges common to every image that undergoes the standardization process.
The method applies a piecewise linear transformation to the image that aligns the intensity histogram to the
histogram taken as reference. The reference histogram has been selected from a high quality image not containing
artificial objects such as coils or stents. This is a pre-processing step that allows employing a training set
built on a limited number of standardized images for the segmentation of standardized images which were not part of the training set. The effectiveness of the presented IIS technique in combination with a well-validated knowledge-based vasculature segmentation method is quantified on a variety of 3DRA images depicting cerebral arteries and intracranial aneurysms. The proposed IIS method offers a solution to the standardization of tissue classes in routine medical images and effectively improves automation and usability of knowledge-based vascular segmentation algorithms.