CN100465990C - An intelligent positioning method for microfluidic chips - Google Patents
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Abstract
本发明涉及一种面向微流控芯片的智能定位方法,使用电荷藕合器件图像传感器采集微流控芯片分析系统中的的芯片平面图像,利用形态学滤波器以及单次扫描细化算法对芯片平面图进行去噪和细化,得到芯片平面图像的骨架图;依据芯片骨架图,提取微流控芯片中微管道网络上的相关节点;根据得到的所有微管道节点位置及其相互之间的连通关系,生成邻接表;然后利用邻接表对微流控芯片进行智能跟踪定位,通过自行设计的一种独特的反馈算法依照定位结果对邻接表进行反馈修正。它是基于相关图像处理技术设计的,具有定位的全自动化、精度高、速度快以及可以跟踪定位等特点,能对各类微流控芯片分析系统进行自动化分析的改进。
The invention relates to an intelligent positioning method for microfluidic chips, which uses a charge-coupled device image sensor to collect a chip plane image in a microfluidic chip analysis system, and uses a morphological filter and a single-scan refinement algorithm to fine-tune the chip. The planar image is denoised and refined to obtain the skeleton diagram of the chip planar image; based on the chip skeleton diagram, the relevant nodes on the micropipeline network in the microfluidic chip are extracted; according to the obtained positions of all micropipeline nodes and their interconnections Then, the adjacency list is used to intelligently track and locate the microfluidic chip, and a unique feedback algorithm designed by itself is used to feedback and correct the adjacency list according to the positioning result. It is designed based on relevant image processing technology, and has the characteristics of fully automatic positioning, high precision, fast speed, and tracking and positioning. It can improve the automatic analysis of various microfluidic chip analysis systems.
Description
技术领域 technical field
本发明涉及智能模式识别与图像处理技术领域,涉及一种面向微流控芯片的智能定位方法。The invention relates to the technical field of intelligent pattern recognition and image processing, and relates to an intelligent positioning method for microfluidic chips.
背景技术 Background technique
微流控芯片分析系统又称微全分析系统,是由瑞士Ciba Geigy公司的Manz与Widmer在上世纪90年代初开始研究的,当时主要的研究重点是微流控芯片分析系统的“微”与“全”,以及微管道网络的MEMS加工方法。到1994年,美国橡树岭国家实验室Ramsey等在Manz的工作基础上发表了一系列论文,改进了芯片毛细管电泳的进样方法,提高了其性能与实用性,微流控芯片分析系统的商业开发价值开始显现。近年来,国际上关于微流控芯片的研究逐步成为热点,并正在加速向微型化和智能化发展。The microfluidic chip analysis system, also known as the micro-full analysis system, was researched by Manz and Widmer of Ciba Geigy in Switzerland in the early 1990s. At that time, the main research focus was on the "micro" and "Full", and MEMS processing methods for microchannel networks. By 1994, Ramsey of Oak Ridge National Laboratory in the United States published a series of papers on the basis of Manz's work, which improved the sampling method of chip capillary electrophoresis, improved its performance and practicability, and commercialized the microfluidic chip analysis system. The development value begins to appear. In recent years, international research on microfluidic chips has gradually become a hot spot, and is accelerating towards miniaturization and intelligence.
现阶段微全分析系统中对芯片的主要分析方法是激光诱导荧光法,这种方法需要对芯片上的微管道网络进行定位,而当前的微全分析系统基本上都采用手工的定位方式,这种定位方式存在有定位精度低、耗时费力以及无法完成跟踪定位等缺点。At present, the main analysis method for chips in the micro-total analysis system is the laser-induced fluorescence method. This method needs to locate the micro-pipe network on the chip. However, the current micro-total analysis systems basically use manual positioning. This positioning method has disadvantages such as low positioning accuracy, time-consuming and laborious, and inability to complete tracking and positioning.
发明内容 Contents of the invention
本发明的目的在于克服现有技术的不足,提供一种面向微流控芯片的智能定位方法,将其应用于各类微流控芯片分析系统中,对电荷藕合器件图像传感器采集到的微流控芯片平面图像根据相关算法生成邻接表,利用邻接表中的数据,对微流控芯片进行智能定位,并依照定位的结果,对邻接表中的数据进行动态的修正。The purpose of the present invention is to overcome the deficiencies of the prior art, provide an intelligent positioning method for microfluidic chips, apply it to various microfluidic chip analysis systems, The planar image of the fluidic chip generates an adjacency list according to the relevant algorithm, uses the data in the adjacency list to intelligently locate the microfluidic chip, and dynamically corrects the data in the adjacency list according to the positioning result.
为了实现上述目的,本发明采用了如下技术方案:In order to achieve the above object, the present invention adopts the following technical solutions:
一种面向微流控芯片的智能定位方法,其特征在于,包括下列步骤:An intelligent positioning method for microfluidic chips, characterized in that it comprises the following steps:
a.使用电荷藕合器件图像传感器采集微流控芯片分析系统中的的芯片平面图像,利用形态学滤波器以及单次扫描细化算法对芯片平面图进行去噪和细化,得到芯片平面图像的骨架图:a. Use the charge-coupled device image sensor to collect the chip plane image in the microfluidic chip analysis system, use the morphological filter and the single-scan thinning algorithm to denoise and refine the chip plane image, and obtain the chip plane image Skeleton diagram:
1)对电荷藕合器件图像传感器采集到的芯片平面图像进行二值化处理;1) Perform binarization processing on the chip plane image collected by the charge-coupled device image sensor;
2)对二值化后的芯片平面图像利用形态学中的开启和闭合操作进行去噪和边缘平滑处理;2) Perform denoising and edge smoothing processing on the binarized chip plane image using the opening and closing operations in morphology;
3)对去噪后的图像利用单次扫描细化算法进行细化,得到芯片平面图像的骨架图;3) Thinning the image after denoising by using a single-scan thinning algorithm to obtain the skeleton diagram of the planar image of the chip;
b.依据芯片骨架图,提取微流控芯片中微管道网络上的相关节点:b. According to the chip skeleton diagram, extract the relevant nodes on the micropipeline network in the microfluidic chip:
1)微管道网络上端点的提取:在芯片骨架图中,某个像素点的8-邻域内有且只有一个像素点存在,则这个像素点就是端点;1) Extraction of endpoints on the micropipeline network: in the chip skeleton diagram, if there is only one pixel in the 8-neighborhood of a certain pixel, then this pixel is an endpoint;
2)微管道网络上交叉点的提取:在芯片骨架图中,某个像素点的8-邻域内有3个或3个以上的像素点,则这个像素点就是交叉点;2) Extraction of intersection points on the micropipeline network: in the chip skeleton diagram, if there are 3 or more pixel points in the 8-neighborhood of a certain pixel point, then this pixel point is an intersection point;
3)微管道网络上拐点的提取:3) Extraction of inflection points on the micro-pipeline network:
这里对芯片骨架图进行雷登变换(Radon Transform),取图像的中心点作为原点,x轴与图像的上边界平行并通过原点,y轴与x轴相互垂直;将其在与x坐标轴夹角为0°~179°度的180个方向上进行投影,每一个角度上的变换结果作为一个列向量,所有的结果组合在一起可以形成一个700×180的变换矩阵,找出变换矩阵中的峰值,这些峰值对应着图像上的直线;Here, Radon Transform is performed on the chip skeleton diagram, and the center point of the image is taken as the origin, the x-axis is parallel to the upper boundary of the image and passes through the origin, and the y-axis and the x-axis are perpendicular to each other; The projection is performed in 180 directions with angles from 0° to 179°, and the transformation result of each angle is used as a column vector. All the results can be combined to form a 700×180 transformation matrix. Find out the transformation matrix Peaks, these peaks correspond to straight lines on the image;
然后,确定所求出的端点和交叉点分别属于哪条直线,若相互连通的一个端点和一个交叉点,端点所属的直线没有通过交叉点,交叉点同时属于多条直线,即可判定这个端点和交叉点之间存在一个拐点;然后将端点所属的直线与交叉点所属的所有直线的交点都求出来,落在端点与交叉点连接线上的交点即为拐点;Then, determine which straight line the obtained endpoint and intersection point belong to. If an endpoint and an intersection point are connected to each other, the line to which the endpoint belongs does not pass through the intersection point, and the intersection point belongs to multiple straight lines at the same time, then the endpoint can be determined. There is an inflection point between the end point and the intersection point; then find the intersection points of the straight line to which the end point belongs and all the straight lines to which the intersection point belongs, and the intersection point falling on the connecting line between the end point and the intersection point is the inflection point;
c.微流控芯片平面图邻接表的生成:c. Generation of adjacency list of microfluidic chip plan:
根据上面得到的所有微管道节点位置及其相互之间的连通关系,可以生成一张相应的邻接表;邻接表中的每一个单元项由一个三元组组成,其中,图像的中心点为原点,前两项表示节点的坐标,第3项表示节点的类型,其中,0表示端点,1表示交叉点,2表示拐点;依据这张邻接表,系统就能够智能地对微流控芯片连续地的跟踪定位。According to the positions of all micro-pipeline nodes obtained above and their mutual connectivity, a corresponding adjacency list can be generated; each unit item in the adjacency list consists of a triplet, where the center point of the image is the origin , the first two items represent the coordinates of the node, and the third item represents the type of the node, among which, 0 represents the endpoint, 1 represents the intersection point, and 2 represents the inflection point; according to this adjacency list, the system can intelligently continuously tracking location.
上述的面向微流控芯片的智能定位方法,其中,所述微流控芯片智能定位与邻接表的反馈修正方法如下:The above-mentioned intelligent positioning method for microfluidic chips, wherein the intelligent positioning of the microfluidic chip and the feedback correction method of the adjacency table are as follows:
1)在用激光诱导荧光的方法对微流控芯片进行分析时,高压电极一定加在两个端点上,故在进行跟踪检测时,激光发射器和光电倍增管也必然是从一个端点移动到另一个端点;依照邻接表可以很容易得到需要进行检测的端点的位置以及端点到端点间的通路;通路中任意两个相邻节点包括端点、交叉点或拐点之间都是直线段,故任意两端点间的通路可以用一组直线段即一条折线段来表示;要对两个端点之间的通路进行跟踪定位,就是要对通路中的每一条直线段进行跟踪定位,而直线段上所有像素点的坐标可以由线段两端的节点坐标求出:设两个节点的坐标分别为(x1,y1)和(x2,y2),则直线段上要确定坐标的像素点数目为:1) When using the method of laser-induced fluorescence to analyze the microfluidic chip, the high-voltage electrode must be added to the two endpoints, so when performing tracking detection, the laser emitter and the photomultiplier tube must also move from one endpoint to the other. Another endpoint; according to the adjacency table, the position of the endpoint to be detected and the path between the endpoint and the endpoint can be easily obtained; any two adjacent nodes in the path, including endpoints, intersections or inflection points, are straight segments, so any The path between the two ends can be represented by a group of straight line segments, that is, a broken line segment; to track and locate the path between the two end points, it is necessary to track and locate each straight line segment in the path, and all the straight line segments The coordinates of the pixel points can be obtained from the coordinates of the nodes at both ends of the line segment: if the coordinates of the two nodes are (x 1 , y 1 ) and (x 2 , y 2 ), then the number of pixels on the line segment whose coordinates need to be determined is :
其中,第i个像素点的坐标为:Among them, the coordinates of the i-th pixel point are:
应用这种方法,就能对整个微流控芯片实现智能的跟踪定位;By applying this method, intelligent tracking and positioning of the entire microfluidic chip can be realized;
2)根据跟踪定位的反馈结果对邻接表进行反馈修正:在一条直线段上进行跟踪定位时,如果在某个像素点处光电倍增管没有接收到相应的荧光信号,就在该像素点的不包括直线段上的点的16-邻域中进行搜索:i)如果没有像素点能接收到荧光信号,则保持原像素点坐标不变;ii)如果只有一个像素点能接收到荧光信号,则用这个像素点的坐标替代原像素点坐标;iii)如果有两个以上像素点能接收到荧光信号,则用这些像素点中沿直线段方向距离原像素点最近的那个像素点的坐标替代原像素点坐标。在对这条直线段跟踪定位完后,同时也对线段上所有像素点的坐标进行了一次校正,然后可以利用下面两个公式来修正邻接表中节点的坐标:2) The adjacency list is corrected according to the feedback results of tracking and positioning: when tracking and positioning on a straight line segment, if the photomultiplier tube at a certain pixel does not receive the corresponding fluorescent signal, it will Search in the 16-neighborhood including the points on the straight line segment: i) if no pixel can receive the fluorescent signal, keep the coordinates of the original pixel unchanged; ii) if only one pixel can receive the fluorescent signal, then Replace the coordinates of the original pixel with the coordinates of this pixel; iii) If there are more than two pixels that can receive the fluorescent signal, use the coordinates of the pixel closest to the original pixel along the direction of the straight line segment among these pixels to replace the original pixel. Pixel coordinates. After tracking and positioning the straight line segment, the coordinates of all pixel points on the line segment are also corrected once, and then the coordinates of the nodes in the adjacency list can be corrected by using the following two formulas:
(x1′,y1′)和(x2′,y2′)将作为(x1,y1)和(x2,y2)两个节点的新坐标去修正邻接表;(x 1 ′, y 1 ′) and (x 2 ′, y 2 ′) will be used as the new coordinates of the two nodes (x 1 , y 1 ) and (x 2 , y 2 ) to correct the adjacency list;
公式中,(x0,y0)表示直线段校正后的中点坐标:In the formula, (x 0 , y 0 ) represents the midpoint coordinates of the straight line segment after correction:
其中,(x′i,y′i)(i=1,…,num)表示校正后的直线段上像素点的坐标。Wherein, (x′ i , y′ i ) (i=1, .
由于采用了上述的技术方案,本发明与现有技术相比,具有以下的优点和积极效果:Owing to adopting above-mentioned technical scheme, the present invention has following advantage and positive effect compared with prior art:
由于本发明面向微流控芯片的智能定位方法,是基于相关图像处理技术设计的,具有定位的全自动化、精度高、速度快以及可以跟踪定位等特点,能对各类微流控芯片分析系统进行自动化分析的改进。Since the intelligent positioning method for microfluidic chips of the present invention is designed based on related image processing technology, it has the characteristics of fully automatic positioning, high precision, fast speed, and tracking and positioning, and can analyze various types of microfluidic chip analysis systems. Improvements to automated analysis.
该方法利用形态学算法、细化算法及雷登变换等相关图像处理方法将电荷藕合器件图像传感器采集到的微流控芯片平面图上微管道的节点提取出来,生成邻接表,根据邻接表对微流控芯片进行智能的跟踪定位,并能通过相关的反馈算法依照定位结果对邻接表进行反馈修正。从而,有效地克服了目前采用手工的定位方式的微全分析系统所存在的定位精度低、耗时费力以及无法完成跟踪定位等缺陷。This method uses morphological algorithm, thinning algorithm, and Wright transform and other related image processing methods to extract the nodes of micropipes on the plane map of the microfluidic chip collected by the charge-coupled device image sensor, and generates an adjacency list. The microfluidic chip performs intelligent tracking and positioning, and can feedback and correct the adjacency list according to the positioning results through relevant feedback algorithms. Thus, it effectively overcomes the shortcomings of low positioning accuracy, time-consuming and labor-intensive, and inability to complete tracking and positioning existing in the current micro-full analysis system that uses manual positioning.
附图说明 Description of drawings
通过以下实施例并结合其附图的描述,可以进一步理解其发明的目的、具体结构特征和优点。附图中,Through the description of the following embodiments combined with the accompanying drawings, the purpose, specific structural features and advantages of the invention can be further understood. In the attached picture,
图1为本发明的微流控芯片智能定位系统的系统框图;Fig. 1 is a system block diagram of the microfluidic chip intelligent positioning system of the present invention;
图2为本发明的微流控芯片平面图像预处理示意图;Fig. 2 is a schematic diagram of the preprocessing of the plane image of the microfluidic chip of the present invention;
图3为本发明端点、交叉点、拐点的特征图;Fig. 3 is the feature figure of endpoint, cross point, inflection point of the present invention;
图4为本发明的整体智能定位方法流程图;Fig. 4 is a flow chart of the overall intelligent positioning method of the present invention;
表1为本发明的微流控芯片邻接表。Table 1 is the adjacency list of the microfluidic chip of the present invention.
图1中,In Figure 1,
1.电荷藕合器件图像传感器;2.图像采集卡;3.计算机;4.RS232-RS485转换器;5.步进电机控制平台;6.芯片放置平台;7.激光发射器;8.高压产生设备;9.光电倍增管;10.PCI总线;11.RS232;12.RS485。1. Charge-coupled device image sensor; 2. Image acquisition card; 3. Computer; 4. RS232-RS485 converter; 5. Stepper motor control platform; 6. Chip placement platform; 7. Laser transmitter; 8. High voltage Generating equipment; 9. Photomultiplier tube; 10. PCI bus; 11. RS232; 12. RS485.
图2中,In Figure 2,
a.二值化后的微流控芯片平面图;a. The plan view of the microfluidic chip after binarization;
b.经形态学噪声滤除器作用后微流控芯片平面图;b. Plan view of the microfluidic chip after the action of the morphological noise filter;
c.经过形态学噪声滤除器作用后的图像细化结果;c. The image thinning result after the action of the morphological noise filter;
d.未经形态学噪声滤除器作用后的图像细化结果。d. Image thinning result without morphological noise filter.
图3中,A.端点;B.交叉点;C.拐点。In Fig. 3, A. end point; B. intersection point; C. inflection point.
具体实施方式 Detailed ways
下面结合具体实施例,进一步阐述本发明。应理解,这些实施例仅用于说明本发明而不用于限制本发明的范围。此外应理解,在阅读了本发明讲授的内容之后,本领域技术人员可以对本发明作各种改动或修改,这些等价形式同样落于本申请所附权利要求书所限定的范围。Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.
本发明的一种面向微流控芯片的智能定位方法包括下列步骤:An intelligent positioning method for microfluidic chips of the present invention comprises the following steps:
1.对当前微流控芯片平面图像进行预处理:1. Preprocess the plane image of the current microfluidic chip:
a.使用电荷藕合器件图像传感器采集微流控芯片分析系统中的芯片平面图像,利用形态学滤波器以及单次扫描细化算法对芯片平面图进行去噪和细化,得到芯片平面图像的骨架图:a. Use the charge-coupled device image sensor to collect the chip plane image in the microfluidic chip analysis system, use the morphological filter and the single-scan thinning algorithm to denoise and refine the chip plane image, and obtain the skeleton of the chip plane image picture:
对电荷藕合器件图像传感器采集到的芯片平面图像进行二值化处理;Perform binarization processing on the chip plane image collected by the charge-coupled device image sensor;
对二值化后的芯片平面图像利用形态学中的开启和闭合操作进行去噪和边缘平滑处理;Denoising and edge smoothing are performed on the binarized chip planar image using the opening and closing operations in morphology;
对去噪后的图像用单次扫描细化算法进行细化,得到芯片平面图像的骨架图。The denoised image is thinned with a single-scan thinning algorithm to obtain the skeleton image of the planar image of the chip.
b.依据芯片骨架图,提取微流控芯片中微管道网络上的相关节点:b. According to the chip skeleton diagram, extract the relevant nodes on the micropipeline network in the microfluidic chip:
微管道网络上端点的提取:在芯片骨架图中,某个象素点的8-邻域内有且只有一个像素点存在,则这个像素点就是端点。Extraction of endpoints on the micropipeline network: In the chip skeleton diagram, if there is one and only one pixel in the 8-neighborhood of a certain pixel, then this pixel is an endpoint.
微管道网络上交叉点的提取:在芯片骨架图中,某个像素点的8-邻域内有3个或3个以上的像素点,则这个像素点就是交叉点。Extraction of intersection points on the micropipeline network: In the chip skeleton diagram, if there are 3 or more pixel points in the 8-neighborhood of a certain pixel point, then this pixel point is an intersection point.
微管道网络上拐点的提取:Extraction of inflection points on a micropipeline network:
这里对芯片骨架图进行雷登变换,取图像的中心点作为原点,x轴与图像的上边界平行并通过原点,y轴与x轴相互垂直。将其在与x坐标轴夹角为0°~179°的180个方向上进行投影,每一个角度上的变换结果作为一个列向量,所有的结果组合在一起可以形成一个700×180的变换矩阵,找出变换矩阵中的峰值,这些峰值对应着图像上的直线。通过这种方法得出骨架线上所有的直线位置。Here, Wrighten transform is performed on the chip skeleton diagram, and the center point of the image is taken as the origin, the x-axis is parallel to the upper boundary of the image and passes through the origin, and the y-axis and x-axis are perpendicular to each other. Project it in 180 directions with an angle of 0° to 179° with the x-coordinate axis, and the transformation result at each angle is used as a column vector, and all the results can be combined to form a 700×180 transformation matrix , find the peaks in the transformation matrix that correspond to the straight lines on the image. In this way, all straight-line positions on the skeleton line are obtained.
然后可以确定上面求出的端点和交叉点分别属于哪条直线,如果相互连通的一个端点和一个交叉点,端点所属的直线没有通过交叉点(交叉点同时属于多条直线),那么可以肯定这个端点和交叉点之间存在一个拐点。然后将端点所属的直线与交叉点所属的所有直线的交点都求出来,那个落在端点与交叉点连接线上的交点就是拐点。通过这种方法,可以将骨架线上的所有拐点求出来。Then it can be determined which straight line the endpoint and the intersection point obtained above belong to. If an endpoint and an intersection point are connected to each other, the straight line to which the endpoint belongs does not pass through the intersection point (the intersection point belongs to multiple straight lines at the same time), then this can be determined. There is an inflection point between the endpoint and the intersection. Then find the intersection points of the straight line to which the endpoint belongs and all the straight lines to which the intersection point belongs, and the intersection point that falls on the connecting line between the endpoint and the intersection point is the inflection point. By this method, all inflection points on the skeleton line can be obtained.
c.微流控芯片平面图邻接表的生成:c. Generation of adjacency list of microfluidic chip plan:
根据上面得到的所有微管道节点位置及其相互之间的连通关系,可以生成一张相应的邻接表。邻接表中的每一个单元项由一个三元组组成,其中,图像的中心点为原点,前两项表示节点的坐标,第三项表示节点的类型,其中,0表示端点,1表示交叉点,2表示拐点。依据这张邻接表,系统就能够智能地对微流控芯片连续的跟踪定位。A corresponding adjacency list can be generated according to the positions of all micro-pipeline nodes obtained above and the interconnection relationship between them. Each unit item in the adjacency list consists of a triplet, in which the center point of the image is the origin, the first two items represent the coordinates of the node, and the third item represents the type of node, where 0 represents the endpoint and 1 represents the intersection point , 2 represents the inflection point. According to this adjacency table, the system can intelligently and continuously track and locate the microfluidic chip.
2.微流控芯片智能定位与邻接表的反馈修正方法,如下:2. The intelligent positioning of the microfluidic chip and the feedback correction method of the adjacency table are as follows:
1)在用激光诱导荧光的方法对微流控芯片进行分析时,高压电极一定加在两个端点上,故在进行跟踪检测时,激光发射器和光电倍增管也必然是从一个端点移动到另一个端点。依照邻接表可以很容易得到需要进行检测的端点的位置以及端点到端点间的通路。参见图3,通路中任意两个相邻节点包括端点、交叉点或拐点之间都是直线段,故任意两端点间的通路可以用一组直线段即一条折线段来表示。要对两个端点之间的通路进行跟踪定位,其实就是要对通路中的每一条直线段进行跟踪定位,而直线段上所有像素点的坐标可以由线段两端的节点坐标求出:设两个节点的坐标分1) When using the method of laser-induced fluorescence to analyze the microfluidic chip, the high-voltage electrode must be added to the two endpoints, so when performing tracking detection, the laser emitter and the photomultiplier tube must also move from one endpoint to the other. Another endpoint. According to the adjacency list, the position of the endpoint to be detected and the path between the endpoints can be easily obtained. Referring to FIG. 3 , any two adjacent nodes in the path, including endpoints, intersections or inflection points, are straight line segments, so the path between any two end points can be represented by a set of straight line segments, that is, a polyline segment. To track and locate the path between two endpoints, in fact, it is necessary to track and locate each straight line segment in the path, and the coordinates of all pixels on the straight line segment can be obtained from the node coordinates at both ends of the line segment: Set two Coordinate points of nodes
别为(x1,y1)和(x2,y2),则直线段上要确定坐标的像素点数目为:are (x 1 , y 1 ) and (x 2 , y 2 ), then the number of pixels on the line segment whose coordinates need to be determined is:
其中第i个像素点的坐标为:The coordinates of the i-th pixel point are:
应用这种方法,就能对整个微流控芯片实现智能的跟踪定位。By applying this method, intelligent tracking and positioning of the entire microfluidic chip can be realized.
2)考虑到用电荷藕合器件图像传感器采集微流控芯片平面图和后续的图像处理过程可能产生一些误差,使得最终的邻接图不一定十分精确,可以采用以下方法根据跟踪定位的反馈结果对邻接表进行反馈修正:在一条直线段上进行跟踪定位时,如果在某个像素点处光电倍增管没有接收到相应的荧光信号,就在该像素点的不包括直线段上的点的16-邻域中进行搜索:i)如果没有像素点能接收到荧光信号,则保持原像素点坐标不变;ii)如果只有一个像素点能接收到荧光信号,则用这个像素点的坐标替代原像素点坐标;iii)如果有两个以上像素点能接收到荧光信号,则用这些像素点中沿直线段方向距离原像素点最近的那个像素点的坐标替代原像素点坐标。在对这条直线段跟踪定位完后,同时也对线段上所有像素点的坐标进行了一次校正,然后可以利用下面两个公式来修正邻接表中节点的坐标:2) Considering that some errors may occur in the acquisition of the microfluidic chip plan and the subsequent image processing by the charge-coupled device image sensor, the final adjacency graph may not be very accurate. Table for feedback correction: when tracking and positioning on a straight line segment, if the photomultiplier tube does not receive the corresponding fluorescent signal at a certain pixel point, the 16-neighbors of the pixel point not including the point on the straight line segment Search in the domain: i) If no pixel can receive the fluorescent signal, keep the coordinates of the original pixel unchanged; ii) If only one pixel can receive the fluorescent signal, replace the original pixel with the coordinates of this pixel Coordinates; iii) If there are more than two pixels that can receive fluorescent signals, replace the coordinates of the original pixel with the coordinates of the pixel closest to the original pixel along the direction of the straight line segment among these pixels. After tracking and positioning the straight line segment, the coordinates of all pixel points on the line segment are also corrected once, and then the coordinates of the nodes in the adjacency list can be corrected by using the following two formulas:
(x1′,y1′)和(x2′,y2′)将作为(x1,y1)和(x2,y2)两个节点的新坐标去修正邻接表。(x 1 ′, y 1 ′) and (x 2 ′, y 2 ′) will be used as the new coordinates of the two nodes (x 1 , y 1 ) and (x 2 , y 2 ) to modify the adjacency list.
公式中,(x0,y0)表示直线段校正后的中点坐标:In the formula, (x 0 , y 0 ) represents the midpoint coordinates of the straight line segment after correction:
其中,(x′i,y′i)(i=1,…,num)表示校正后的直线段上像素点的坐标。Wherein, (x′ i , y′ i ) (i=1, .
关于本发明的邻接表生成相关原理简述如下:About the adjacency list generation relevant principle of the present invention is briefly described as follows:
单次扫描细化算法可以很好地将图像细化成单象素宽,不破坏原始图像的连通性,较好地保持图像的拓扑结构。图像中的节点(端点,交叉点以及拐点)在细化后的骨架图中将表现出比较明显的特征,可以很方便地将它们依照一定的规则提取出来。The single-scan thinning algorithm can finely thin the image into a single pixel width without destroying the connectivity of the original image, and better maintain the topological structure of the image. The nodes (endpoints, intersections and inflection points) in the image will show more obvious features in the thinned skeleton diagram, and they can be easily extracted according to certain rules.
雷登变换就是将原始图像变换为它在各个角度的投影表示。图像的投影是指图像在某一方向上的线积分,对于数字图像来说也就是在该方向上的累加求和。雷登变换的数学表示是:图像f(x,y)在任一角度θ上R的投影定义为:The Wryden transform is to transform the original image into its projection representation at various angles. The projection of an image refers to the line integral of the image in a certain direction. For digital images, it is also the cumulative summation in this direction. The mathematical representation of the Wright transformation is: the projection of the image f(x, y) on any angle θ R is defined as:
其中,in,
雷登变换后很容易找出骨架图像中的直线,从而方便拐点的提取。It is easy to find out the straight line in the skeleton image after Wrighten transform, so as to facilitate the extraction of inflection points.
关于本发明的面向微流控芯片的智能定位方法的工作原理About the working principle of the intelligent positioning method for microfluidic chips of the present invention
微流控芯片智能定位系统的组成如图1所示,主要由步进电机控制平台、电荷藕合器件图像传感器、图像采集卡、激光发射器、光电倍增管、高压产生设备以及计算机组成。在现有微流控芯片分析系统的基础上增加了电荷藕合器件图像传感器图像采集机构,用于拍摄系统中微流控芯片的平面图,并通过图像采集卡传送到计算机。图像采集卡与计算机通过PCI总线相连。激光发射器、光电倍增管、高压产生设备以及步进电机控制平台都是通过串行口RS485经RS232/RS485转换器与计算机相连接。The composition of the microfluidic chip intelligent positioning system is shown in Figure 1. It is mainly composed of a stepping motor control platform, a charge-coupled device image sensor, an image acquisition card, a laser transmitter, a photomultiplier tube, a high-voltage generating device, and a computer. On the basis of the existing microfluidic chip analysis system, an image acquisition mechanism of a charge-coupled device image sensor is added to take a plan view of the microfluidic chip in the system and transmit it to the computer through an image acquisition card. The image acquisition card is connected with the computer through the PCI bus. The laser transmitter, photomultiplier tube, high-voltage generating equipment and stepping motor control platform are all connected to the computer through the serial port RS485 through the RS232/RS485 converter.
同时,本发明基于以下认识:Simultaneously, the present invention is based on following recognition:
微流控芯片上微管道网络中的微管道的拓扑结构均是直线,不存在拓扑结构为曲线或其它非直线类别线型的微管道。微流控芯片无论以何种角度放置在系统中,均认为其原点在电荷藕合器件图像传感器采集到的微流控芯片平面图的正中心。The topological structure of the micropipes in the micropipe network on the microfluidic chip is all straight lines, and there is no micropipe whose topological structure is curved or other non-straight lines. Regardless of the angle at which the microfluidic chip is placed in the system, its origin is considered to be at the exact center of the planar view of the microfluidic chip collected by the charge-coupled device image sensor.
本发明的一种面向微流控芯片的智能定位方法,包括下列步骤:An intelligent positioning method for microfluidic chips of the present invention comprises the following steps:
1.对当前微流控芯片平面图像进行预处理,参见图2:1. Preprocess the plane image of the current microfluidic chip, see Figure 2:
a.使用电荷藕合器件图像传感器采集微流控芯片分析系统中的的芯片平面图像,利用形态学滤波器以及单次扫描细化算法对芯片平面图进行去噪和细化,得到芯片平面图像的骨架图:a. Use the charge-coupled device image sensor to collect the chip plane image in the microfluidic chip analysis system, use the morphological filter and the single-scan thinning algorithm to denoise and refine the chip plane image, and obtain the chip plane image Skeleton diagram:
对电荷藕合器件图像传感器采集到的芯片平面图像进行二值化处理;Perform binarization processing on the chip plane image collected by the charge-coupled device image sensor;
对二值化后的芯片平面图像利用形态学中的开启和闭合操作进行去噪和边缘平滑处理;Denoising and edge smoothing are performed on the binarized chip planar image using the opening and closing operations in morphology;
对去噪后的图像用单次扫描细化算法进行细化,得到芯片平面图像的骨架图。The denoised image is thinned with a single-scan thinning algorithm to obtain the skeleton image of the planar image of the chip.
b.依据芯片骨架图,提取微流控芯片中微管道网络上的相关节点,参见图3:b. According to the chip skeleton diagram, extract the relevant nodes on the micropipeline network in the microfluidic chip, see Figure 3:
微管道网络上端点的提取:在芯片骨架图中,某个像素点的8-邻域内有且只有一个像素点存在,则这个像素点就是端点。Extraction of endpoints on the micropipeline network: In the chip skeleton diagram, if there is one and only one pixel in the 8-neighborhood of a certain pixel, then this pixel is an endpoint.
微管道网络上交叉点的提取:在芯片骨架图中,某个像素点的8-邻域内有3个或3个以上的像素点,则这个像素点就是交叉点。Extraction of intersection points on the micropipeline network: In the chip skeleton diagram, if there are 3 or more pixel points in the 8-neighborhood of a certain pixel point, then this pixel point is an intersection point.
微管道网络上拐点的提取:Extraction of inflection points on a micropipeline network:
这里对芯片骨架图进行雷登变换,取图像的中心点作为原点,x轴与图像的上边界平行并通过原点,y轴与x轴相互垂直。将其在与x坐标轴夹角为0°~179°的180个方向上进行投影,每一个角度上的变换结果作为一个列向量,所有的结果组合在一起可以形成一个700×180的变换矩阵,找出变换矩阵中的峰值,这些峰值对应着图像上的直线。通过这种方法得出骨架线上所有的直线位置。Here, Wrighten transform is performed on the chip skeleton diagram, and the center point of the image is taken as the origin, the x-axis is parallel to the upper boundary of the image and passes through the origin, and the y-axis and x-axis are perpendicular to each other. Project it in 180 directions with an angle of 0° to 179° with the x-coordinate axis, and the transformation result at each angle is used as a column vector, and all the results can be combined to form a 700×180 transformation matrix , find the peaks in the transformation matrix that correspond to the straight lines on the image. In this way, all straight-line positions on the skeleton line are obtained.
然后可以确定上面求出的端点和交叉点分别属于哪条直线,如果相互连通的一个端点和一个交叉点,端点所属的直线没有通过交叉点(交叉点同时属于多条直线),那么可以肯定这个端点和交叉点之间存在一个拐点。然后将端点所属的直线与交叉点所属的所有直线的交点都求出来,那个落在端点与交叉点连接线上的交点就是拐点。通过这种方法,可以将骨架线上的所有拐点求出来。Then it can be determined which straight line the endpoint and the intersection point obtained above belong to. If an endpoint and an intersection point are connected to each other, the straight line to which the endpoint belongs does not pass through the intersection point (the intersection point belongs to multiple straight lines at the same time), then this can be determined. There is an inflection point between endpoints and intersections. Then find the intersection points of the straight line to which the endpoint belongs and all the straight lines to which the intersection point belongs, and the intersection point that falls on the connecting line between the endpoint and the intersection point is the inflection point. By this method, all inflection points on the skeleton line can be obtained.
c.微流控芯片平面图邻接表的生成,参见表1:c. Generation of the adjacency list of the planar graph of the microfluidic chip, see Table 1:
根据上面得到的所有微管道节点位置及其相互之间的连通关系,可以生成一张相应的邻接表。邻接表中的每一个单元项由一个三元组组成,其中,图像的中心点为原点,前两项表示节点的坐标,第三项表示节点的类型,其中,0表示端点,1表示交叉点,2表示拐点。依据这张邻接表,系统就能够智能地对微流控芯片连续的跟踪定位。A corresponding adjacency list can be generated according to the positions of all micro-pipeline nodes obtained above and the interconnection relationship between them. Each unit item in the adjacency list consists of a triplet, in which the center point of the image is the origin, the first two items represent the coordinates of the node, and the third item represents the type of node, where 0 represents the endpoint and 1 represents the intersection point , 2 represents the inflection point. According to this adjacency table, the system can intelligently and continuously track and locate the microfluidic chip.
2.微流控芯片智能定位与邻接表的反馈修正方法,如下:2. The intelligent positioning of the microfluidic chip and the feedback correction method of the adjacency table are as follows:
1)在用激光诱导荧光的方法对微流控芯片进行分析时,高压电极一定加在两个端点上,故在进行跟踪检测时,激光发射器和光电倍增管也必然是从一个端点移动到另一个端点。依照邻接表可以很容易得到需要进行检测的端点的位置以及端点到端点间的通路。通路中任意两个相邻节点(端点、交叉点或拐点)之间都是直线段,故任意两端点间的通路可以用一组直线段(即一条折线段)来表示。要对两个端点之间的通路进行跟踪定位,其实就是要对通路中的每一条直线段进行跟踪定位,而直线段上所有像素点的坐标可以由线段两端的节点坐标求出:设两个节点的坐标分别为(x1,y1)和(x2,y2),则直线段上要确定坐标的像素点数目为:1) When using the method of laser-induced fluorescence to analyze the microfluidic chip, the high-voltage electrode must be added to the two endpoints, so when performing tracking detection, the laser emitter and the photomultiplier tube must also move from one endpoint to the other. Another endpoint. According to the adjacency list, the position of the endpoint to be detected and the path between the endpoints can be easily obtained. There are straight line segments between any two adjacent nodes (end points, intersections or inflection points) in the path, so the path between any two ends can be represented by a set of straight line segments (that is, a polyline segment). To track and locate the path between two endpoints, in fact, it is necessary to track and locate each straight line segment in the path, and the coordinates of all pixels on the straight line segment can be obtained from the node coordinates at both ends of the line segment: Set two The coordinates of the nodes are (x 1 , y 1 ) and (x 2 , y 2 ), then the number of pixels on the straight line segment whose coordinates need to be determined is:
其中第i个像素点的坐标为:The coordinates of the i-th pixel point are:
应用这种方法,就能对整个微流控芯片实现智能的跟踪定位。By applying this method, intelligent tracking and positioning of the entire microfluidic chip can be realized.
2)考虑到用电荷藕合器件图像传感器采集微流控芯片平面图和后续的图像处理过程可能产生一些误差,使得最终的邻接图不一定十分精确,可以采用以下方法根据跟踪定位的反馈结果对邻接表进行反馈修正:在一条直线段上进行跟踪定位时,如果在某个像素点处光电倍增管没有接收到相应的荧光信号,就在该像素点的不包括直线段上的点16-邻域中进行搜索:i)如果没有像素点能接收到荧光信号,则保持原像素点坐标不变;ii)如果只有一个像素点能接收到荧光信号,则用这个像素点的坐标替代原像素点坐标;iii)如果有两个以上像素点能接收到荧光信号,则用这些像素点中沿直线段方向距离原像素点最近的那个像素点的坐标替代原像素点坐标。在对这条直线段跟踪定位完后,同时也对线段上所有像素点的坐标进行了一次校正,然后可以利用下面两个公式来修正邻接表中节点的坐标:2) Considering that some errors may occur in the acquisition of the microfluidic chip plan and the subsequent image processing by the charge-coupled device image sensor, the final adjacency graph may not be very accurate. Table for feedback correction: When tracking and positioning on a straight line segment, if the photomultiplier tube does not receive the corresponding fluorescent signal at a certain pixel point, the point 16-neighborhood of the pixel point that does not include the straight line segment Search in: i) If no pixel can receive the fluorescent signal, keep the coordinates of the original pixel unchanged; ii) If only one pixel can receive the fluorescent signal, replace the coordinates of the original pixel with the coordinates of this pixel ; iii) If there are more than two pixel points that can receive fluorescent signals, replace the coordinates of the original pixel point with the coordinates of the pixel point closest to the original pixel point along the direction of the straight line segment among these pixel points. After tracking and positioning the straight line segment, the coordinates of all pixel points on the line segment are also corrected once, and then the coordinates of the nodes in the adjacency list can be corrected by using the following two formulas:
(x1′,y1′)和(x2′,y2′)将作为(x1,y1)和(x2,y2)两个节点的新坐标去修正邻接表。(x 1 ′, y 1 ′) and (x 2 ′, y 2 ′) will be used as the new coordinates of the two nodes (x 1 , y 1 ) and (x 2 , y 2 ) to modify the adjacency list.
公式中,(x0,y0)表示直线段校正后的中点坐标:In the formula, (x 0 , y 0 ) represents the midpoint coordinates of the straight line segment after correction:
其中,(x′i,y′i)(i=1,…,num)表示校正后的直线段上像素点的坐标。Wherein, (x′ i , y′ i ) (i=1, .
综合上述步骤,本发明的整体流程图如图4所示。Combining the above steps, the overall flow chart of the present invention is shown in FIG. 4 .
表一:Table I:
表1 Table 1
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