Magnetic resonance angiography (MRA) has become a common way to study cerebral vascular structures. Indeed, it enables to obtain information on flowing blood in a totally non-invasive and non-irradiant fashion. MRA exams are generally performed for three main applications: detection of vascular pathologies, neurosurgery planning, and vascular landmark detection for brain functional analysis. This large field of applications justifies the necessity to provide efficient vessel segmentation tools. Several methods have been proposed during the last fifteen years. However, the obtained results are still not fully satisfying. A solution to improve brain vessel segmentation from MRA data could consist in integrating high-level a priori knowledge in the segmentation process. A preliminary attempt to integrate such knowledge is proposed here. It is composed of two methods devoted to phase contrast MRA (PC MRA) data. The first method is a cerebral vascular atlas creation process, composed of three steps: knowledge extraction, registration, and data fusion. Knowledge extraction is performed using a vessel size determination algorithm based on skeletonization, while a topology preserving non-rigid registration method is used to fuse the information into the atlas. The second method is a segmentation process involving adaptive sets of gray-level hit-or-miss operators. It uses anatomical knowledge modeled by the cerebral vascular atlas to adapt the parameters of these operators (number, size, and orientation) to the searched vascular structures. These two methods have been tested by creating an atlas from a 18 MRA database, and by using it to segment 30 MRA images, comparing the results to those obtained from a region-growing segmentation method.

Purpose:To propose an atlas-based method that uses both phase and magnitude images to integrate anatomical information in order to improve the segmentation of blood vessels in cerebral phase-contrast magnetic resonance angiography (PC-MRA). Material and Methods:An atlas of the whole head was developed to store the anatomical information. The atlas divides a magnitude image into several vascular areas, each of which has specific vessel properties. It can be applied to any magnitude image of an entire or nearly entire head by deformable matching, which helps to segment blood vessels from the associated phase image. The segmentation method used afterwards consists of a topology-preserving, region-growing algorithm that uses adaptive threshold values depending on the current region of the atlas. This algorithm builds the arterial and venous trees by iteratively adding voxels that are selected according to their grayscale value and the variation of values in their neighborhood. The topology preservation is guaranteed because only simple points are selected during the growing process. Results:The method was performed on 40 PC-MRA images of the brain. The results were validated using maximumintensity projection (MIP) and three-dimensional surface rendering visualization, and compared with results obtained with two non-atlas-based methods. Conclusion:The results show that the proposed method significantly improves the segmentation of cerebral vascular structures from PC-MRA. These experiments tend to prove that the use of vascular atlases is an effective way to optimize vessel segmentation of cerebral images. MAGNETIC RESONANCE ANGIOGRAPHY (MRA) is a noninvasive process (1) that provides three-dimensional images of vascular structures. The two main kinds of techniques that have been designed to visualize venous and arterial blood vessels are time-of-flight (TOF) MRA (2) and phase-contrast (PC) MRA (3). These techniques are frequently used to study the vascular structures of the brain. Indeed, obtaining precise information about brain vessels is fundamental for planning and performing neurosurgical procedures, as well as for detecting pathologies such as aneurysms and stenoses.Several methods devoted to vascular network segmentation from three-dimensional angiographic data have been published during the last 15 years. They can be divided into eight categories, corresponding to the main strategies used to carry out the segmentation, as follows: filtering, mathematical morphology, regiongrowing, vessel tracking, differential analysis, deformable models, statistical analysis, and artificial intelligence. It should be noted that the proposed classification is not the only one that can be used. Indeed, many other criteria can be chosen to classify the existing algorithms, including automation, the kind of data being processed, centerline or whole-vessel detection, size of the searched vessels, general methods, or methods devoted to a particular organ. The following text describes only a small part of the existing methods for ...