医学专业外文翻译基于OPTA细化算法的有关脑动脉瘤检测的研究.doc

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1、Research on Cerebral Aneurysm DetectionBased on OPTA AlgorithmJian Wu, Guangming Zhang, Jie Xia, and Zhiming CuiProceedings of the 2009 International Symposium on Information ProcessingHuangshan, P. R. China, August 21-23, 2009, pp. 037-040基于OPTA细化算法的有关脑动脉瘤检测的研究吴建,张广明,谢杰,崔志明2009年8月21日 23日中国黄山2009年信息

2、处理国际研讨会论文集 037页040页摘 要在脑动脉瘤识别系统中,这是能够准确、快速地在图像中定位出脑动脉瘤位置的关键步骤。经过了脑动脉瘤的形态特征的详细分析后,本文提出了一种新的脑动脉瘤检测方法,这种检测方法是基于改进了的细化算法。在这种新的检测方法中,首先利用OPTA细化算法提取出血管的骨架,然后根据血管的骨架信息来检测脑动脉瘤。经过大量实验之后发现,脑动脉瘤识别过程在以这种新的检测方法为前提的情况下,可以很好的检测到脑动脉瘤的位置。关键词: OPTA, 细化算法, 脑动脉瘤检测, 模板匹配.前言脑血管疾病,尤其是脑动脉瘤,是导致成年人生病最后死亡的关键因素之一,它严重威胁着人们得生命安全

3、。随着计算机技术的不断发展和成熟,信息技术和医学成像技术结合而产生的CAD(应用计算机辅助诊断)系统在脑血管疾病的检测与治疗中起到越来越重要的作用,它已经成为了医学成像上的一个研究重点。脑血管瘤一般位于血管的交叉位置,尤其是在脑动脉周围。原因是血液的流动对血管交叉位置的影响很大。脑血管的影响类似于河流形成的网络,会出现许多分支的动脉血管。一般来说,血管是对称的,它的两侧的血管壁是相互平行的。而脑动脉瘤是由于血管壁损坏而导致的突出的部分。脑动脉瘤的示意图如图1所示。图1 脑动脉瘤的示意图如图1,图中用方格标志的地方就是脑动脉瘤。正常的血管出已经形成了突出的部分,大致平行的血管壁被破坏了。被破坏的

4、位置明显是在交叉处。所以我们可以确定脑动脉瘤的位置应该在血管骨架结构的交叉处。在基于DSA(数字减法血管造影术)的脑动脉瘤CAD系统中,它是特征提取及识别的前提和重要步骤,应用在检测脑动脉瘤位置的DSA中。本文分析了脑动脉瘤的形态特征,包括通过细化算法得到的血管骨架的拓扑结构,然后对骨架树进行深度优先遍历,最后定位出脑动脉瘤的位置。.OPTA算法OPTA(一次通过细化算法)是一种典型的基于模板的图像细化算法,其核心是通过应用消去和保留模板实现细化过程。OPTA是一个迭代的过程。如果当前的点满足消去模板同时不满足保留模板,那么这个点就被消去,否则,保留此点。不断遍历原始图片,直到再没有点满足上述

5、要求。对OPTA算法的主要改进的地方就在于根据原算法的效果和速度增加了消去和保留模板。其中最典型的参考文件是13。文献1的作者发现原始的OPTA算法细化的不完整,有一定的缺陷,细化出的图像有毛刺且不够平滑。改进的算法中已经将消去和保留模板进行了改善。图2和图3是改进了的消去和保留模板。其中有阴影的方格是当前点。图2 消去模板1图3 保留模板1从文献1可以看出,在原始算法中,保留模板太松弛了,分析图像可以得知,会容易产生右斜线,且使迭代次数会增加,速度会变慢。因此,在文献1的基础上突出了新的保留模板,并增至9个。从图4可以看出,保留模板的条件变得更加严格,从而解决了右斜线的问题,速度也有明显的改

6、善。图4 保留模板2通过深入的改进,由袁梅和其他作者发现的右斜线的影响已经被文献2中的算法很好的解决了,且速度有了很大的提升。由于增强了的保留条件,使得图像出现了断点。从而导致了拓扑结构和原始图像被破坏了,细化算法也被减弱了。在以上分析的基础上,袁梅和其他的作者提出了组合模板的概念,利用文献1的消去模板,放弃其中的保留模板,并且结合了对当前点的判断,判断当前点是否符合消去模板,符合的话保留它相邻点的位置。图5是当前点以及他的邻接点的示意图。图5 点p的邻接点基于组合模板的细化算法不需要匹配保留模板的操作。每个消去模板对应着一种情况,并且每个消去模板利用“&”和“|”操作来完成判断当前点是否要被

7、保留。下面的8个条件式对应图2中ah的8个消去模板。如果该点满足消去模板以及消去条件,则消去此点。否则暴力此点。(q4=0 & q5=0 & q6=0 & q7=0) | (q5=1 & q6=1 & q12=0 & q13=0 & q14=0) (1)(q2=1 & q4=0 & q7=0 & q9=1 & q11=0) | (q2=0 & q9=0 & q12=0 & q13=0) (2)(q5=0 & q6=1 & q7=0 & q11=0) (3)(q2=0 & q9=1 & q13=0 & q14=0) (4)(q8=1 & q10=0 & q13=0 & q14=0) | ( q8

8、=0 & q10=1 & q12=0) (5)(q3=0 & q4=0 & q7=0 & q10=1) | (q3=1 & q7=0 & q10=0 & q11=0) |(q8=1 & q10=0 & q13=0 & q14=0) | (q8=0 & q10=1 & q13=0 & q14=0) (6) (q3=0 & q4=0 & q7=0 & q10=1 & q11=0) | ( q3=1 & q7=0 & q10=0 & q11=0) (7)p=1 (8) 其中,p是当前点,图5显示了q1q15。虽然目前,改进的OPTA算法只应用于指纹图像的识别,文献18。本文应用上述的公式和改进的OP

9、TA来进行脑血管图像骨架的提取。通过实验发现,通过原OPTA算法细化的图像有一些毛刺,因此不能用于脑动脉瘤的检测。文献1的效果更佳,但是细化速度不是很理想。尽管文献2中的细化算法的速度有了明显的提高,但细化血管过程中断点容易产生。衡量了各种因素,我们决定以组合模板的改进OPTA算法来实现脑血管骨架的提取。由此方法得到的血管骨架有很高的质量,同时,速度也得到了加强。.基于骨架树的脑动脉瘤的检测在通过细化脑血管图像得到的骨架树后,原本具有一定宽度的血管变成只有一个像素宽度的骨架,同时脑血管图像变成了单像素宽度的曲线。通过提取骨架结构元素,脑动脉瘤的检测就是基于骨架树中分支元素的宽度。A. 骨架结构

10、元素的提取1)关键点元素的提取关键点体现在骨架特征发生变化的地方,包括分叉点和端点。端点就是骨架树的某部分的起始点,当前点p的8个邻接点只有一个是骨架中的点,那么此点p就是端点,像图6中的点a,c,d和f。分叉点就是骨架树的不同部分的交点,当前点p的8个邻接点只有三个或者更多的点是骨架中的点,那么此点p就是交叉点,像图6中的点b,e。图6 骨架元素的表示2)分支元素的提取分支元素是骨架段,它连接着两个关键点,并且不通过骨架中的第三个关键点。如果关键点不是端点,那么它叫做内部分支点,否则叫做外部分支点。本文中,我们讨论的分支元素是外部分支点。分支元素的提取方法是从端点开始:先找到端点,跟踪此点,

11、直到下一个分叉处。从端点开始到分叉处结束的骨架元素即是分支元素。B. 基于分支元素的脑动脉瘤的检测通过分析脑动脉瘤的形态得出结论:如何提取脑血管骨架的分支结构是检测脑动脉瘤的关键。脑动脉瘤,毛刺和普通血管分别会使骨骼图像出现三种分支结构。所以脑动脉瘤可以通过从三种分支结构中判断是否是由脑动脉瘤引起的分支检测出来。在本文中,通过深度遍历骨架树,分支的像素宽度是检测的基础。具体步骤如下进行。(1) 从根部遍历骨架树,标记访问过的像素;(2) 一旦遇到分叉点P,则按垂直方向逆时针访问它8领域中的邻接点,把没有访问的点放入栈中。(3) 从栈中获取一个元素作为开始点,以上述方法继续遍历骨架树中的像素,直

12、到所有点都遍历完。同时用变量S来计算从开始到结束所访问的像素数。如果S大于阈值T1小于阈值T2,则曲线的位置就是脑动脉瘤的一部分,如果S小于T1,则认为是毛刺,S大于T2则认为是普通的血管。(4) 重复上述过程,直到栈空为止。则骨架树就遍历完了。上述过程中,分支位置的检测方法是以当前点为中心,检查它的8邻域中目标点和未被访问的点数。根据经验值,T1设置为8,T2设置为16,这样在某种程度上就可以消去毛刺,同时可以消去对检测脑动脉瘤的干扰。可以获得最好的效果。.实验与分析系统运行的硬件环境用的是CPU:P(R)4 2.8兆赫;内存:512M;显卡:128M。开发环境是VC 6.0。脑血管DSA图

13、像的实验数据是苏州大学第一附属医院提供的符合DICOM 3.0标准的图像。通过运用开发软件架构DICOM,每一幅DSA图像都被分成DSA图像序列,保存为BMP格式。在实验中,我们先对图1(a)和图1(b)原始图像做二值化,然后运用改进的细化算法来获得图像的骨架,最后利用基于骨架分支的脑动脉瘤检测算法来检测整个骨架图。最后用方格标记疑似脑动脉瘤的区域。普通脑血管图像的血管结构相对简单,并且图像边缘相对平滑,如图7(a);图7(b)中显示的血管结构图,由于图像的复杂和模糊度导致了血管不平滑,且有噪音干扰。图7 原始图像二值化效果图图8是分支检测和基于分支特征的脑动脉瘤检测方法的最终结果图。图8 脑

14、动脉瘤的检测结果图本文采用了基于改进了的组合模板的细化算法来检测二值图像的分支,它有效的避免了毛刺的出现。这是一种效果较好的算法,图8是不同效果的对比,图8(a)毛刺较少,图8(b)毛刺较多。经过大量的实验,发现,不理想的二值化效果以及噪声的影响和其他原因导致了容易出现毛刺。由于毛刺会出现在相同的分支结构中,因此,它大大影响了脑动脉瘤检测的精度。如图8所示,用方格标记的位置是检测到的脑动脉瘤的位置。图8(a)中的检测效果很好,图8(b)中检测出的区域有两处是错误的。本文中的基于脑动脉瘤骨架特征检测的方法基本上可以定位出脑动脉瘤的位置。尽管基于分支长度的检测会遇到干扰,最后导致出现不正确的检测,

15、但是脑动脉瘤检测的意义是在于减少目标数量并为识别过程提供有用的信息。如何进行有效的二值化,为骨架提取提供基础,同时减少毛刺对脑动脉瘤检测的比利影响将是今后研究的重点。.结论本文分析了脑动脉瘤的形态特征,有关OPTA算法以及改进的算法的深入研究,并且利用它们成功提取出了图像的骨架。提出了一种基于骨架信息的脑动脉瘤检测方法,它是通过分支结果元素长度来实现检测的。实验结果显示,通过从骨架中提取分支元素后,用此方法可以检测出脑动脉瘤的区域。此外,检测效果还与脑血管分割效果和骨架的提取有关。怎样克服这些缺点,提高检测的成功率是今后工作的重点。声明这次研究由中国自然科学基金会(60673092)以及江苏省

16、现代信息技术应用软件工程研究中心No.SX200803赞助的。参考文献1 Feng Xing-kui, Li Lin-yan, Yan Zu-quan, “A New Thinning Algorithm for Fingerprint Image,” Journal of Image and Graphics, 1999, 4(10):835-838.2 WANG Jia-long, GUO Cheng-an, “An Improved Image Template Thinning Algorithm,” Journal of Image and Graphics, 2004, 3(9):

17、297-301.3 MEI Yuan, SUN Huai-jiang, XIA De-shen, “An Improved Template-based Rapid Thinning Algorithm,” Journal of Image and Graphics, 2006, 11(9):1306-1311.4 TAN Tai-zhe, NING Xin-bao, YIN Yi-long, “A Method for Singularity Detection in Fingerprint Images,” Journal of Software, 2003, 14(6):1082-108

18、8.5 LIANG Guang-min, CAI Xue-jun, “Improvement of OPTA algorithm and its application in fingerprint images thinning,” Computer Engineering and Design, 2006,27(23):4607-4608.6 Zhang Yu-Jin, Image Engineering (II): Image Analysis(second edition), BeiJing:Tsinghua University Press, 2005.7 XU Gang-feng,

19、 WANG Ping, SHEN Zhen-kang,“Recognition of main-runway of airport based on skeleton,” Infrared and Laser Engineering, 2006,35(6):717-721.8 ZHANG Jin-yang, SUN Mao-hang, “Study on algorithm for skeleton features extraction of hand vein image,”Journal of Computer Applications, 2007, 27(1):152-154.Rese

20、arch on Cerebral Aneurysm DetectionBased on OPTA AlgorithmAbstractIt 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 prop

21、osed 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 lot

22、s of experiments, the cerebral aneurysm can be detected well by using this new method, which provides a premise for cerebral aneurysm recognition.Index TermsOPTA, thinning algorithm, cerebral aneurysm detection, template matchingI. INTRODUCTIONCerebral vascular disease, especially cerebral aneurysm

23、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 computer technology, CAD(Computeraided Diagnosis) System resulting from the combination of information technology and medical imag

24、ing technology plays a more and more important role in detecting and treating cerebral vascular disease, and it has already become 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 re

25、ason is that the impact of blood flow has great influence on the bifurcation position. Cerebral vascular image is similar to river network, and there exists artery and many other branch vessels. Generally, vessel is approximately symmetric, whose two edge contours are approximately mutual parallel.

26、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

27、 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.Figure 1. The schematic diagram of cerebral aneurysmIn the cerebral aneurysm CAD system based o

28、n DSA(Digital Subtracted Angiography), it is the premise and important step of feature extraction and recognition to detect the position of cerebral aneurysm in DSA. This paper analyzes morphological characteristics of cerebral aneurysm, obtains the topological skeleton tree via the improved thinnin

29、g algorithm, then does depth-first traversal search on skeleton tree, and lastly detects the position of cerebral aneurysm.II. OPTA ALGORITHMOPTA(One-pass Thinning Algorithm) is a typical template-based image thinning algorithm, whose core is thinning processing by the application of elimination tem

30、plate and preservation template. OPTA is an iterative process. If the current point satisfies the elimination template and doesnt satisfy preservation template, it will be eliminated. Otherwise, it will be preserved. The original image is traversalled continuously until no point satisfies the above

31、condition. The main improvement upon OPTA is proposing new elimination template and preservation template according to the defect of thinning effect and thinning speed of original algorithm. Among them, the most typical documents are 13. It is found by the authors of document 1 that original OPTA ha

32、s the disadvantages of incomplete thinning, more burr and inadequate smooth. Much improvement has been undergone on elimination template and preservation template in the improved algorithm. Figure 2 and Figure 3 are the improved elimination template and preservation template respectively, among whic

33、h shadow pane is current point.Figure 2. The elimination template of document 1Figure 3. The preservation template of document 1Because the condition for preservation template in the algorithm of document 1 is too loose, the effect of right oblique line produces easily while analyzing the images, wh

34、ich made iteration times increase and the speed slow. Therefore, new preservation template was proposed on the basis of document 1 and they were increased to 9. From Figure 4, the condition of preservation template becomes more strictly, which solved the effect of right oblique line well, and the sp

35、eed has been improved immensely.Figure 4. The preservation template of document 2With the development to the depth, Mei Yuan and other authors found that the effect of right oblique line was solved well by the algorithm of document 2 and the speed has been improved immensely. But because of its enha

36、nced preservation condition, the breakpoints appeared. As a result, the topology and connectivity of original image has been destroyed and the thinning effect decreased. On the basis of the above analysis, Mei Yuan and other authors proposed the concept of composition template, which took advantage

37、of the elimination template of document 1, abandoned the preservation template and combined the judgment whether the point satisfied the elimination template will be preserved with the situation of its neighborhood point. Figure 5 is the schematic diagram about the neighborhood of current point.Figu

38、re 5. The neighborhood diagram of point pThe thinning algorithm based on improved composition template doesnt need matching operation for preserve template. Every elimination template corresponds to one case of every neighborhood points pixel value, and it uses logical operation “&” and “|” to compl

39、ete the judgment whether current point will be preserved. The following 8 conditions correspond to 8elimination template marked with ah in Figure2. If it satisfies elimination template and corresponding elimination condition, this point will be deleted. Otherwise, it will be preserved.(q4=0 & q5=0 &

40、 q6=0 & q7=0) | (q5=1 & q6=1 & q12=0 & q13=0 & q14=0) (1)(q2=1 & q4=0 & q7=0 & q9=1 & q11=0) | (q2=0 & q9=0 & q12=0 & q13=0) (2)(q5=0 & q6=1 & q7=0 & q11=0) (3)(q2=0 & q9=1 & q13=0 & q14=0) (4)(q8=1 & q10=0 & q13=0 & q14=0) | ( q8=0 & q10=1 & q12=0) (5)(q3=0 & q4=0 & q7=0 & q10=1) | (q3=1 & q7=0 & q

41、10=0 & q11=0) |(q8=1 & q10=0 & q13=0 & q14=0) | (q8=0 & q10=1 & q13=0 & q14=0) (6) (q3=0 & q4=0 & q7=0 & q10=1 & q11=0) | ( q3=1 & q7=0 & q10=0 & q11=0) (7)p=1 (8) Among them, p is the current point and Figure 5 shows q115. Though at present, improved OPTA is only applied to the thinning of fingerpr

42、int image 48. This paper applied the above and improved OPTA to skeleton extraction of cerebral vascular image. By many experiments, we found that the burr of vascular skeleton extracted by original OPTA is a bit more, so it cannot be used in the detection of cerebral aneurysm. The effect of documen

43、t 1 is much better, but the thinning speed is not ideal. Though the thinning speed of document 2 wasincreased significantly, the breakpoint phenomenon produces easily while thinning vessel. Through weighing various factors, we determine that our work of extracting cerebral vascular skeleton was carr

44、ied out on the basis of improved OPTA using composition template. The vascular skeleton gained by this algorithm has high quality; meanwhile, the speed is also enhanced.III. DETECTION OF CEREBRAL ANEURYSM BASED ON SKELETON TREEAfter obtaining the skeleton tree by thinning the cerebral vascular image

45、, a certain width of the blood vessel becomes only a pixel width of the skeleton and cerebral vascular image becomes a curve of single pixel. Through the extraction of skeleton structure elements, thedetection of cerebral aneurysm is based on the length of branch element on skeleton tree.A. Extracti

46、on of Skeleton Structure Elements1) Extraction of key point elementKey points present to the places where dramatic changes have been taken place in skeleton characteristics, which includes endpoint and bifurcation. Endpoint is the starting point on the part of skeleton tree, and p is endpoint if the

47、re has only one skeleton point in the eight neighborhood of the current point p, just as a,c,d and f shown in Figure 6. Bifurcation is the converging point of different parts of skeleton tree, and p is bifurcation if there has three or more skeleton points in the eight neighborhood of the current point p, just as b and eshown in Figure 6.Figure 6. Skeleton structure elements representati

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