The cerebral vascular system is constituted by all the arteries and veins irrigating the brain. This vascular tree starts from two pairs of arteries, the vertebral arteries and the internal carotid arteries. These latter divide into a circular shape being called the Circle of Willis (CoW). There is considerable variability in the structure of the CoW among patients. The CoW can host various vascular diseases, among which intracranial aneurysms are of particular importance because their occurrence, or more precisely their rupture, can be devastating. Intracranial aneurysms often occur at the bifurcations of the arterial tree (saccular aneurysms), as a bulge in the vessel wall. It is crucial to recognize and monitor such aneurysms. Anatomical identification of the bifurcations of the CoW can be of great help to establish a diagnosis or to plan a surgical operation. In this study, we propose an automatic solution to categorize the vascular anatomy of the CoW in 3D volumes by identifying its main constituting bifurcations. Our solution combines machine learning and a multivariate analysis (Linear Discriminant Analysis: LDA). The LDA works as a classifier and reduces the dimensionality of the dataset by transforming the selected features in a lower dimensional space. This work is a preliminary study prior to moving to human cerebrovascular images. We evaluate the proposed method using several machine learning techniques combined with a leave-one-out validation applied on a set of 30 synthetic vascular images as well as 30 mouse cerebral vasculatures.
KEYWORDS: Aneurysms, Arteries, Blood vessels, 3D metrology, 3D acquisition, Brain, Blood circulation, Computed tomography, 3D image processing, Magnetic resonance imaging
An aneurysm is a vascular disorder represented by a ballooning of a blood vessel. The blood vessel’s wall is distorted by the blood flow, and a bulge forms there. When ruptured, the aneurysm may cause irreversible damage and could even lead to premature death. Intra-cranial aneurysms are the ones presenting the higher risks. In this work, thanks to a graph based approach, we detect the bifurcations located on the circle of Willis within brain mice cerebral vasculature. Once properly located in the 3D stack, we characterize the cerebral arteries bifurcations, i.e. we gather several properties of the bifurcation, such as their angles, or area cross section, in order to further estimate geometrical patterns that can favor the risk of occurrence of an intra-cranial aneurysm. Effectively, apart from genetic predisposition, and environmental risk factors (high blood pressure, smoking habits, ...) the anatomical disposition of the brain vasculature may influence the chances of an aneurysm to form. Our objectives in this paper is to obtain accurate measurements on the 3D bifurcations.
KEYWORDS: 3D image processing, Visualization, Optical spheres, 3D modeling, Zoom lenses, Eye models, Visual process modeling, Visual system, Skull, Human vision and color perception
3D object shapes (represented by meshes) include both areas that attract the visual attention of human observers and others less or not attractive at all. This visual attention depends on the degree of saliency exposed by these areas. In this paper, we propose a technique for detecting salient regions in meshes. To do so, we define a local surface descriptor based on local patches of adaptive size and filled with a local height field. The saliency of mesh vertices is then defined as its degree measure with edges weights computed from adaptive patch similarities. Our approach is compared to the state-of-the-art and presents competitive results. A study evaluating the influence of the parameters establishing this approach is also carried out. The strength and the stability of our approach with respect to noise and simplification are also studied.
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