Numerical simulations, which are based on reliable biomechanical models of blood vessels, can help to get a better understanding of cardiovascular diseases such as atherosclerosis, and can be used to develop optimal medical treatment strategies.

The adventitia is the outer most layer of blood vessels and its mechanical properties are essentially determined by the three‐dimensional, structural arrangement of collagen fibre bundles embedded in the tissue. Global information such as the orientation statistics of the fibre bundles as well as detailed information as the crimp of the single fibres within the bundles is of particular interest in biomechanical modeling.

In order to obtain a sufficiently large amount of data for biomechanical modeling, a fully automatic method for the structural analysis of the soft tissue is required. In this contribution we present methods based on computer vision to fulfill this task. We start by discussing proper tissue preparation and imaging techniques that have to be used to obtain data, which reliably represents the real three‐dimensional tissue structure. The next step is concerned with algorithms that robustly segment the collagen fibre bundles and cope with various kinds of artifacts. Novel segmentation techniques for robust segmentation of individual fibril bundles and methods for estimation of their parameters, such as location, shape, mean fibril orientation, crimp of fibrils, etc, is discussed. The proposed algorithms are based on novel perceptual grouping methods operating on the extracted orientation data of fibrils.

Finally, we demonstrate the results obtained by our fully automatic method on real data. In addition, for a more quantitative assessment, we introduce a generative structural model that enables the synthesis of three‐dimensional fibre bundles with well‐defined characteristics.