【導讀】經過了腦動脈瘤的形態(tài)特征的詳細分析后，本文提出了一種新的腦動脈瘤檢測。在這種新的檢測方法中，首先利用OPTA. 細化算法提取出血管的骨架，然后根據(jù)血管的骨架信息來檢測腦動脈瘤。重威脅著人們得生命安全。越重要的作用，它已經成為了醫(yī)學成像上的一個研究重點。腦血管瘤一般位于血管的交叉位置，尤其是在腦動脈周圍。原因是血液的流動對血。腦血管的影響類似于河流形成的網絡，會出現(xiàn)許多分支的動脈。一般來說，血管是對稱的，它的兩側的血管壁是相互平行的。血管壁損壞而導致的突出的部分。如圖1，圖中用方格標志的地方就是腦動脈瘤。正常的血管出已經形成了突出的部。分，大致平行的血管壁被破壞了。動脈瘤的位置應該在血管骨架結構的交叉處。前提和重要步驟，應用在檢測腦動脈瘤位置的DSA中。模板進行了改善。的保留模板，并增至9個。法的速度有了明顯的提高，但細化血管過程中斷點容易產生。很高的質量，同時，速度也得到了加強。

　　

【正文】 binarization segmentation on original images Figure 1(a) and Figure 1(b) firstly, then obtain its skeleton by using improved thinning algorithm, and detect the skeleton map using the cerebral aneurysm detection method based on the branches of skeleton map, lastly mark the suspected region of cerebral aneurysm with pane. The vascular structure of original cerebral vascular image in Figure 1(a) is relatively simple, and the image edge is smooth after binarization segmentation, which shown in Figure 7(a)。 while in Figure 1(b) the plexity and fuzziness of vascular structure causes the inadequate smooth and noise jamming of blood vessel after binarization segmentation, which shown in Figure 7(b). Figure 7. The effect diagram of the original image after binarization Segmentation Figure 8 is the final effect diagram of skeleton extraction and cerebral aneurysm detection 18 method based on skeleton features. Figure 8. The effect drawing of cerebral aneurysm detection This paper adopts the thinning algorithm based on improved position template to extract the skeleton of binary image, which can effectively overe the emergence of burr. It39。s a betterperforming algorithm, but the contrast in the experiment appears different effects of skeleton extraction, Figure 8(a) with less burrs of skeleton, while Figure 8(b) with more burrs in skeleton. After a large number of experiments, the reason is found that the emergence of burr is largely due to the inadequate binary effect of blood vessels, noise jamming and other reasons. Burr in the vascular skeleton structure also manifested as the same branch structure, so it affects on the precision of cerebral aneurysm detection greatly. As shown in Figure8, the position marked by pane is the detected cerebral aneurysm part. The detection effect in Figure8 (a) is good, while two wrong regions exist in Figure 8(b). In this paper, the cerebral aneurysm detection method based on skeleton features can basically determine the position of cerebral aneurysm. Although the length of branch used as a basis will encounter the interference of skeleton burr and exist incorrect detection, the significance of cerebral aneurysm detection is to reduce the numbers of detection objectives of cerebral aneurysm and provide data premise for recognition. How to do binarization effectively, provide best foundation to skeleton extraction, and reduce adverse effect on cerebral aneurysm detection caused by burr will be the future research emphasis. V. CONCLUSION This paper analyzes the morphological characteristics of cerebral aneurysm, researches OPTA and its improved algorithms deeply and applies them to the extraction of skeleton image successfully. A detection method of cerebral aneurysm based on skeleton is proposed, which detected cerebral aneurysm by the length of branch element. Experimental results show that 19 the regions of cerebral aneurysm can be detected by this method after the extraction of branch elements on skeleton map. Furthermore, the accuracy of detection is affected by the effect of cerebral vascular segmentation and the extraction of skeleton. How to overe these shortings and improve the success rate of detection is the focus of future works. ACKNOWLEDGMENT This research was partially supported by the National Natural Science Foundation of China(60673092) and the opening project of JiangSu Province Support Software Engineering Ramp。D Center for Modern Information Technology Application in Enterprise under grant . REFERENCES [1] Feng Xingkui, Li Linyan, Yan Zuquan, “A New Thinning Algorithm for Fingerprint Image,” Journal of Image and Graphics, 1999, 4(10):835838. [2] WANG Jialong, GUO Chengan, “An Improved Image Template Thinning Algorithm,” Journal of Image and Graphics, 2020, 3(9):297301. [3] MEI Yuan, SUN Huaijiang, XIA Deshen, “An Improved Templatebased Rapid Thinning Algorithm,” Journal of Image and Graphics, 2020, 11(9):13061311. [4] TAN Taizhe, NING Xinbao, YIN Yilong, “A Method for Singularity Detection in Fingerprint Images,” Journal of Software, 2020, 14(6):10821088. [5] LIANG Guangmin, CAI Xuejun, “Improvement of OPTA algorithm and its application in fingerprint images thinning,” Computer Engineering and Design, 2020,27(23):46074608. [6] Zhang YuJin, Image Engineering (II): Image Analysis(second edition), BeiJing:Tsinghua University Press, 2020. [7] XU Gangfeng, WANG Ping, SHEN Zhenkang,“Recognition of mainrunway of airport based on skeleton,” Infrared and Laser Engineering, 2020,35(6):717721. [8] ZHANG Jinyang, SUN Maohang, “Study on algorithm for skeleton features extraction of hand vein image,”Journal of Computer Applications, 2020, 27(1):152154.