【正文】 普通腦血管圖像的血管結(jié)構(gòu)相對簡單，并且圖像邊緣相對平滑 ，如圖 7(a)；圖 7(b)中顯示的血管結(jié)構(gòu)圖，由于 圖像的復(fù)雜和模糊度導(dǎo)致了血管不平滑，且有噪音干擾。 8 圖 7 原始圖像二值化效果 圖 圖 8是分支檢測和基于分支特征的腦動脈瘤檢測方法的最終結(jié)果圖 。 圖 8 腦動脈瘤的檢測結(jié)果圖 本文采用了基于改進了的組合模板的細化算法來檢測二值圖像的分支 ，它有效的避免了毛刺的出現(xiàn)。這是一種效果較好的算法，圖 8是不同效果的對比，圖 8(a)毛刺較少，圖 8(b)毛刺較多。 經(jīng)過大量的實驗，發(fā)現(xiàn)，不理想的二值化效果以及噪聲的影響 和其他原因 導(dǎo)致了容易出現(xiàn)毛刺。 由于毛刺會出現(xiàn)在相同的分支結(jié)構(gòu)中，因此，它大 大影響了腦動脈瘤檢測的精度。 如圖 8所示，用方格標記的位置是檢測到的腦動脈瘤的位置。圖8(a)中的檢測效果很好，圖 8(b)中檢測出的區(qū)域有兩處是錯誤的。 本文中的基于 腦動脈瘤 骨架特征檢測的方法基本上可以定位出腦動脈瘤的位置。 盡管基于分支長度的檢測會遇到干擾，最后導(dǎo)致出現(xiàn)不正確的檢測，但是 腦動脈瘤檢測的意義是在于減少目標數(shù)量并為識別過程提供有用的信息。 如何進行有效的二值化，為骨架提取提供基礎(chǔ)，同時減少毛刺對腦動脈瘤檢測的比利影響將是今后研究的重點。 9 Ⅴ .結(jié)論 本文分析了腦動脈瘤的形態(tài)特征，有關(guān) OPTA算法以及改 進的算法的深入研究，并且利用它們成功提取出了圖像的骨架。 提出了一種基于骨架信息的腦動脈瘤檢測方法，它是通過分支結(jié)果元素長度來實現(xiàn)檢測的。 實驗結(jié)果顯示， 通過從骨架中提取分支元素后，用此方法可以檢測出腦動脈瘤的區(qū)域。 此外，檢測效果還與腦血管分割效果和骨架的提取有關(guān)。 怎樣克服這些缺點，提高檢測的成功率是今后工作的重點。 聲明 這次研究由中國自然科學(xué)基金會 (60673092)以及江蘇省現(xiàn)代信息技術(shù)應(yīng)用軟件工程研究中心 。 10 參考文獻 [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. 11 Research on Cerebral Aneurysm Detection Based on OPTA Algorithm Abstract—It is the key step of the cerebral aneurysm recognition system to locate the cerebral aneurysm accurately and fast onto the image. A new detection method of cerebral aneurysm, which is based on the improved thinning algorithm, is proposed after analyzing the morphological characteristics of cerebral aneurysm fully in the paper. In this new detection method, the improved OPTA algorithm is used to get the skeleton tree of blood vessel firstly, and then cerebral aneurysms are detected by searching the skeleton tree. After doing lots of experiments, the cerebral aneurysm can be detected well by using this new method, which provides a premise for cerebral aneurysm recognition. Index Terms—OPTA, thinning algorithm, cerebral aneurysm detection, template matching I. INTRODUCTION Cerebral vascular disease, especially cerebral aneurysm is one of the key factors leading to disease and death in adults, which threaten the health and life of human badly. With the development and the unceasing maturation of puter technology, CAD(Computeraided Diagnosis) System resulting from the bination of information technology and medical imaging technology plays a more and more important role in detecting and treating cerebral vascular disease, and it has already bee a research focus in medical imaging. The cerebral aneurysm usually was located in the bifurcation position of vessel, especially the cerebral artery circulus, and the reason is that the impact of blood flow has great influence on the bifurcation position. Cerebral vascular image is similar to river work, and there exists artery and many other branch vessels. Generally, vessel is approximately symmetric, whose two edge contours are approximately mutual parallel. But cerebral aneurysm is the projecting part of the vessel edge caused by lesion. The schematic diagram of cerebral aneurysm is showed in Figure 1. From Figure 1, the position marked by pane is cerebral aneurysm. The projecting part has appeared in the normal vessel and the approximately parallel of two edge contours is broken. The lesion site was manifested as obvious branch structure. So we can ascertain the lesion site of cerebral aneurysm by detecting the branch structure in vessel skeleton tree. 12 Figure 1. The schematic diagram of cerebral aneurysm In the cerebral aneurysm CAD system based on DSA(Digital Subtracted Angiography), it is the premise and important step of feature extraction and recognition to detect the position of cerebral