Quantification of morphological change of cerebral blood vessels using three-dimensional fractal analysis and magnetic resonance angiography

磁共振血管造影（MRA）作为一种非侵入性的血管解剖测量方法，目前已发挥了重要作用。在这项研究中，我们开发了一种自动种子区域生长算法从MRA数据中提取血管。在传统的区域生长算法中，用户必须手动将种子点放置在大血管内，并且还输入分割阈值。此外，必须使用不同的种子和阈值集合重复这些过程，直到获得令人满意的结果。因此，该方法是耗时的，并且通过该方法获得的结果对于用户是高度主观的。该算法首先对原始MRA数据进行二值化处理，得到血管的粗略图像，然后利用得到的骨架进行区域生长。该方法能够自动稳定地提取血管，并能很大程度上抑制分割泄漏。总之，我们的自动种子区域生长算法似乎是有用的提取和显示血管。

Magnetic resonance angiography (MRA) has currently played a useful role as a noninvasive method of surveying vascular anatomy. In this study, we developed an automated seeded region growing algorithm for extraction of blood vessles from MRA data. In the conventional region growing algorithm, the user must manually place a seed point within a large blood vessel and also input the segmentation threshold. Furthermore, these processes must be repeated with a different set of seeds and threshold until the satisfactory results are obtained. Thus, this method is time- consuming and the results obtained by this method are highly subjective to the user. With our algorithm, binary images were firstly generated by thresholding the original MRA data to roughly obtained images of blood vessels, and then the skeletons were used for region growing. Our method extracted blood vessels automatically and stably, and the segmentation leakage could be largely suppressed. In conclusion, our automated seeded region growing algorithm appears to be useful for extracting and displaying blood vessels.