The basilar artery is a blood vessel located from the pontomedullary junction to the upper border of the pons. It is an essential blood vessel that supplies blood to the inner brainstem via many blood vessels during blood circulation. When there is an abnormality in the basilar artery, various symptoms occur such as tinnitus, nystagmus, limb ataxia, local paralysis, brainstem infarction, and lesions. Thus, the diagnosis of blood vessels is crucial for diagnosing the present state and future states of a person. However, because there are so many blood vessels in the human body, and the shape of blood vessels varies from person to person, it is very difficult to diagnose blood vessels only from visual information of medical images. This paper deals with geometry analysis and vector analysis as a prerequisite for improving the accuracy of diagnosis of current and future states of a person. Diagnosis of the current state of a person with respect to previous blood vessels includes visual inspection of the cross-sectional medical image, reconstruction in 3D if necessary to determine the shapes of the blood vessels, and identification of the diameter, branch angle and angulation of the blood vessel from the medical images themselves.
In this paper, we introduce image processing techniques such as Canny edge detection, contours, and distance transform for accurate data analysis of blood vessels. Canny edge detection has the advantages of lower error rate and higher accuracy than other edge detection methods, the contours method can accurately connect the contours of the extracted edges, and the distance transform can find the center points of the vessel. Using the data extracted from the above methods, we performed the geometry and vector analysis of the basilar artery. In this paper, we summarized the extracted data using Graph and Table in order to verify the performance of the algorithm.