## 1 Introduction  
Hypertension is one of the severest and most common diseases nowadays. It is considered one of the leading contributors to death worldwide [1]. Severe adverse events of hypertension include strokes, dementia, heart failure, kidney dysfunction, cognitive impairment, and vision loss [2]. According to a 2017 guideline [3], categories of hypertension are as follows: normal (<120/ <80 mmHg), elevated or prehypertension (120-129/ <80 mmHg), hypertension stage 1 (130-139 mmHg systolic blood pressure (BP) or 80-89 mmHg diastolic BP), and hypertension stage 2 (\(\geq\)140 mmHg systolic BP or \(\geq\)90 mmHg diastolic BP). Current blood pressure diagnosis tools such as sphygmomanometers can detect the disease only after its onset. Recent studies observed that cerebral vascular changes start to develop years before the onset of hypertension [4, 5]. Change in blood vessels size has been reported in the literature and linked to elevation of blood pressure in animals [6] and in humans [7]. Vascular tortuosity excess and abnormalities have been correlated to several vascular and non-vascular diseases including hypertension [8, 9]. Additionally, the correlation between cerebral vascular changes and hypertension detection has been studied and validated in [10]. Thus, quantifying and tracking changes in vessels diameters and tortuosity could be used as predictors for hypertension development. Most importantly, the early detection and quantification of these cerebrovascular changes would help physicians to recommend medical protocols to control the development of the disease and hopefully to mitigate adverse events. Nevertheless, the process of extracting and quantifying these features requires the accurate segmentation of cerebral vasculature at first. This makes the extraction process a challenging task because there is a limitation of accurate cerebrovascular segmentation algorithms in the literature. The need for an automatic segmentation algorithm for the brain's vascular system is inevitable. Vascular segmentation is a basic step in almost every medical imaging analysis system. However, segmentation of cerebral blood vessels is a very challenging task due to the complex geometry of human brains' vasculature, the dynamic range of intensities, the density of tiny vessels, in addition to the inherent challenges in the Magnetic Resonance Angiography (MRA) scans such as noise artifacts, bias, and acquisition errors. Unfortunately, existing cerebrovascular segmentation methods suffer from limitations due to one or more of the aforementioned problems. Additionally, most algorithms are not suitable for pathological vessels because of adopted assumption of the vessel circular cross section which is only valid for healthy blood vessels. For instance, [PERSON] et al. [11] have proposed an automatic vascular segmentation algorithm using a deep convolutional neural network (CNN) that was able to segment cerebrovascular with a Dice Similarity Coefficient (DSC) ranging between 0.764 and 0.786. However, their algorithm was tested using MRA dataset of healthy subjects only. [PERSON] et al. [12] have proposed a 2-D segmentation algorithm where they first separated vessels from background using volume projection, 2-D segmentation, and back projection procedures. Then, they utilized a stochastic expectation maximization algorithm to estimate the probability density function of the remaining undetected vessels. While their method was able to detect vessels even in low-contrast images, it was computationally expensive. [PERSON] et al. [13] have proposed a statistical analysis method to segment brain blood vessel based on vessels' intensities. Using vessels' intensities only in the segmentation process is not sufficient due to the wide range of intensity values, in addition to the change of vessels' intensities according to the velocity of the blood flow. Specifically, the velocity of blood flow in vessels, particularly small ones, is slow which makes the intensity of these vessels similar to other brain tissues.