CN115359467A - Identification method and device for deformable medical target - Google Patents
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Abstract
Description
技术领域technical field
本发明涉及一种针对可变形医学靶标的识别方法,属于机器视觉系统技术 领域。The invention relates to a recognition method for deformable medical targets, belonging to the technical field of machine vision systems.
背景技术Background technique
微创穿刺手术是传统开放手术的重大变革。具有手术效率高、术后并发症 少、创伤小和术后恢复快等优点。目前,传统外科穿刺手术在医学影像下依据 医生经验进行穿刺,存在病灶靶点定位偏移的问题。Minimally invasive puncture surgery is a major change from traditional open surgery. It has the advantages of high operation efficiency, less postoperative complications, less trauma and quick postoperative recovery. At present, traditional surgical puncture is performed under medical imaging based on doctor's experience, and there is a problem of lesion target positioning deviation.
现阶段临床中,手术穿刺定位依赖的技术有电磁定位、机械定位、超声定位 和光学定位等方式。At the present stage of clinical practice, surgical puncture positioning relies on electromagnetic positioning, mechanical positioning, ultrasonic positioning, and optical positioning.
机械定位装置能够在一定的程度下起到辅助定位的作用,但是由于装置的存 在,缩小了手术空间,且装置的自由度及灵活性较差,影响手术的实施,同时, 刚性的穿刺路径定位,忽略了组织器官对手术器械形变的影响,易导致手术定 位偏差。The mechanical positioning device can assist positioning to a certain extent, but due to the existence of the device, the operating space is reduced, and the degree of freedom and flexibility of the device is poor, which affects the implementation of the operation. At the same time, the rigid puncture path positioning , ignoring the influence of tissues and organs on the deformation of surgical instruments, which will easily lead to surgical positioning deviation.
超声定位技术在精度和速度上取得了很大的突破,且成本较低,但是位置的 误差判断往往来自环境,包括温度、湿度、气流和噪音等,这些环境因素不易 控制,需要额外使用算法进行相应的补偿。Ultrasonic positioning technology has made great breakthroughs in accuracy and speed, and the cost is low, but the position error judgment often comes from the environment, including temperature, humidity, airflow and noise, etc. These environmental factors are not easy to control, and additional algorithms are required. corresponding compensation.
磁定位导航在微创医疗外科手术领域有着广泛的应用,该方法测量精度高, 重复性好,但磁定位导航易受到病人手术时的身体特征的影响,包括呼吸,心 跳和动作等。同时,磁定位导航不仅成本高,而且作用范围较小,医生的手术 操作范围受限,并且一些必要的磁性器件和磁干扰材料的存在严重影响了定位 精度。Magnetic positioning navigation is widely used in the field of minimally invasive medical surgery. This method has high measurement accuracy and good repeatability. However, magnetic positioning navigation is easily affected by the physical characteristics of the patient during surgery, including breathing, heartbeat and movement. At the same time, magnetic positioning navigation is not only costly, but also has a small range of action, which limits the scope of the doctor's operation, and the existence of some necessary magnetic devices and magnetic interference materials seriously affects the positioning accuracy.
发明内容Contents of the invention
本发明的目的在于克服现有技术中的不足,提供一种针对可变形医学靶标 的识别方法及装置,对遮挡、杂波以及非线性对比度变化是鲁棒的,提高了医 学靶标在变形情况下的识别准确率和速度。The purpose of the present invention is to overcome the deficiencies in the prior art, and provide a method and device for identifying deformable medical targets, which is robust to occlusion, clutter and nonlinear contrast changes, and improves the performance of medical targets under deformation conditions. recognition accuracy and speed.
为达到上述目的,本发明是采用下述技术方案实现的:In order to achieve the above object, the present invention is achieved by adopting the following technical solutions:
第一方面,本发明提供了一种针对可变形医学靶标的识别方法,包括以下 步骤:In a first aspect, the present invention provides a method for identifying a deformable medical target, comprising the following steps:
(a)获取医学靶标模型的图像;(a) acquiring an image of the medical target model;
(b)将靶标的图像变换成与搜索空间的递归细分一致的多级金字塔表示,所 述多级金字塔中至少包括靶标模型的图像;(b) transforming the image of the target into a multi-level pyramid representation consistent with the recursive subdivision of the search space, the multi-level pyramid including at least the image of the target model;
(c)针对搜索空间的每一个离散化级,生成医学靶标的至少一个预先计算的 靶标模型,所述预先计算的靶标模型包括多个具有对应方向矢量的模型点,所 述靶标的模型点和方向矢量通过返回每一个模型点的方向矢量的图像处理操作 产生;(c) For each discretization level of the search space, generate at least one pre-calculated target model of the medical target, the pre-calculated target model includes a plurality of model points with corresponding direction vectors, the model points of the target and Orientation vectors are generated by image processing operations that return an orientation vector for each model point;
(d)将所述模型点细分成多个部分,其中,靶标模型的变形实例通过对所述 部分进行变换来表示;每一个部分由多个点组成,多个部分形成交叠的点集;(d) subdividing the model points into multiple parts, wherein the deformed instance of the target model is represented by transforming the parts; each part is composed of multiple points, and the multiple parts form overlapping point sets ;
(e)获取搜索图像;(e) obtain the search image;
(f)将搜索图像变换成与搜索空间的递归细分一致的多级表示,所述多级表 示至少包括所述搜索图像;(f) transforming the search image into a multilevel representation consistent with a recursive subdivision of the search space, said multilevel representation including at least said search image;
(g)对多级表示的每一个变换的图像执行图像处理操作,所述图像处理操作 返回所述搜索图像内的靶标模型点的子集的方向矢量,该子集对应于搜索所述 至少一个预先计算的靶标模型所应该针对的变换范围;(g) performing an image processing operation on each transformed image of the multilevel representation that returns direction vectors for a subset of target model points within the search image corresponding to the search for the at least one The range of transformations that the precomputed target model should target;
(h)对于靶标模型点进行聚类,形成多个独立局部,每个独立局部在一定范 围内匹配计算得到局部度量结果,通过计算由每个部分中的模型点数量归一化 的结果总和获取全局匹配度量;(h) Cluster the target model points to form multiple independent parts. Each independent part is matched and calculated within a certain range to obtain the local measurement results, which are obtained by calculating the sum of the results normalized by the number of model points in each part. global matching measure;
(i)确定全局匹配度量超过了可选择的阈值且其匹配度量局部最大的靶标模 型姿态,以及,根据该模型姿态产生处于搜索空间的最粗糙的离散化级的所述 至少一个预先计算的靶标模型的实例的列表;(i) determining the target model pose for which the global matching metric exceeds a selectable threshold and whose matching metric is locally maximum, and generating said at least one precomputed target at the coarsest discretization level of the search space based on the model pose a list of instances of the model;
(j)通过获取每一个局部的最佳匹配获得了对变形的估算,计算描述所述部 分的局部位移的变形变换;(j) obtaining an estimate of the deformation by obtaining the best fit for each part, computing a deformation transformation describing the local displacement of said part;
(k)通过搜索空间的递归细分,跟踪处于搜索空间的最粗糙的离散化级的所 述至少一个预先计算的靶标模型的实例,直到达到最精细的离散化级;(k) tracking instances of said at least one precomputed target model at the coarsest discretization level of the search space through recursive subdivision of the search space until the finest discretization level is reached;
(l)在每一级计算相应的变形变换,并将所述变形变换传送到下一级;(l) Computing a corresponding deformation transformation at each stage and passing said deformation transformation to the next stage;
(m)在最精细的离散化级提供靶标模型实例的变形变换和模型姿态。(m) Provides deformation transformations and model poses of target model instances at the finest discretization level.
进一步的,对于可变形的靶标模型,样条函数由位移来定义,针对局部位 移独立搜索每一组次多个点,沿着图像一直进行到达最低金字塔级,在最低金 字塔级,以甚至高于原图像的分辨率确定位移,在亚像素精度的位置实例化, 此时部分位移由梯度幅度限定,以高于最精细的离散化级的分辨率来确定靶标 的位置。Further, for the deformable target model, the spline function is defined by the displacement, and each set of multiple points is searched independently for the local displacement, all the way along the image to the lowest pyramid level, at the lowest pyramid level, even higher than The resolution of the original image determines the displacement, instantiated at locations with sub-pixel accuracy, where partial displacements are bounded by the magnitude of the gradient, and the target position is determined at a resolution higher than the finest discretization level.
进一步的,除了用户可选择的阈值之外,只有可选择的满足变形要求的靶 标假设实例也被生成到最粗糙的离散化级上的可能匹配的列表中。Further, in addition to a user-selectable threshold, only selectable instances of target hypotheses satisfying the deformation requirements are also generated into the list of possible matches at the coarsest discretization level.
进一步的,步骤h中,每一个部分的局部度量结果必须超过用户可选择的 局部阈值,否则,认为该部分被遮挡并且将该部分舍弃而不做进一步的处理。Further, in step h, the local measurement result of each part must exceed the user-selectable local threshold, otherwise, the part is considered to be occluded and the part is discarded without further processing.
进一步的,步骤d中,利用k均值聚类或利用归一化分割来进行细分。Further, in step d, k-means clustering or normalized segmentation is used for subdivision.
进一步的,步骤j中,所计算的变换是透视变换。Further, in step j, the calculated transformation is a perspective transformation.
进一步的,步骤j中,根据接收有关成像装置的内部几何参数和模型的度量 信息作为输入,所计算的变换是三维姿态。Further, in step j, according to receiving as input the internal geometric parameters of the imaging device and the metric information of the model, the calculated transformation is a three-dimensional pose.
进一步的,步骤h中,所变换的靶标模型部分和搜索图像的方向矢量的归 一化点积之和被用于局部度量结果。Further, in step h, the sum of the normalized dot products of the transformed target model part and the direction vector of the search image is used for the local metric result.
进一步的,步骤h中,为了实现相对于对比度反转的不变性,从局部度量 结果中舍弃极性信息,所变换的靶标模型部分和搜索图像的方向矢量的归一化 点积之和的绝对值或归一化点积的绝对值之和被用于局部度量结果。Further, in step h, in order to achieve invariance with respect to contrast inversion, polarity information is discarded from the local metric results, the absolute sum of the normalized dot products of the transformed target model part and the direction vector of the search image The sum of the absolute values of values or normalized dot products is used for the local metric results.
第二方面,本发明提供一种针对可变形医学靶标的识别装置,包括存储在 计算机可读介质上的程序代码装置,用于在所述计算机程序产品在计算机上运 行时执行第一方面所述的针对可变形医学靶标的识别方法。In a second aspect, the present invention provides an identification device for a deformable medical target, comprising program code means stored on a computer-readable medium, for executing the method described in the first aspect when the computer program product is run on a computer. A method for the identification of deformable medical targets.
与现有技术相比,本发明所达到的有益效果:Compared with the prior art, the beneficial effects achieved by the present invention are as follows:
1、本发明提出了在光学成像下准确识别变形医学靶标的识别方法。该方法 结合了不变性匹配度量、将靶标模型分解成多个部分。尽管靶标模型被分解成 子部分,但是用来在最高金字塔级搜索的模型的相关尺寸并没有减小。因此, 本发明没有现有技术方法中所存在的数目减小的金字塔级的速度限制。对部分 遮挡、杂波以及非线性照度变化是鲁棒的;1. The present invention proposes a recognition method for accurately recognizing deformed medical targets under optical imaging. The method incorporates an invariant matching metric, decomposing the target model into parts. Although the target model is decomposed into subparts, the relative size of the model used to search at the highest pyramid level is not reduced. Thus, the present invention does not have the speed limitation of the reduced number of pyramid levels present in prior art methods. Robust to partial occlusion, clutter and nonlinear illumination changes;
2、本方法即使当靶标由于透视变形或更广义的变形而变换时,也能够对靶 标进行识别,提高了靶标识别的准确性与鲁棒性。2. Even when the target is transformed due to perspective deformation or deformation in a wider sense, the method can identify the target, which improves the accuracy and robustness of target identification.
3、本项发明使用的是光学定位法,通过光学仪器导航进行定位,摄像机对 目标场景进行采集,采用图像处理的方法求取物体的三维空间下的姿态,该方 法成本低廉,且精度高,该项针对可变形医学靶标的识别方法,用于存在部分 遮挡、杂波以及非线性照度变化的情况下在图像中检测可变形的医学靶标,可 提高靶标检测效率及准确性。3. This invention uses the optical positioning method. Positioning is performed through optical instrument navigation, the camera collects the target scene, and the image processing method is used to obtain the attitude of the object in three-dimensional space. This method is low in cost and high in accuracy. This identification method for deformable medical targets is used to detect deformable medical targets in images in the presence of partial occlusion, clutter and nonlinear illumination changes, which can improve the efficiency and accuracy of target detection.
附图说明Description of drawings
图1是本发明的一个优选实施例的流程图,示出了所述方法的步骤;Figure 1 is a flow chart of a preferred embodiment of the present invention showing the steps of the method;
图2示出了医学靶标的图像以及用于模型生成的靶标周围的兴趣区域;Figure 2 shows an image of a medical target and the region of interest around the target for model generation;
图3A示出了靶标的模型生成,其中靶标在平面表面上;Figure 3A shows model generation of a target, where the target is on a planar surface;
图3B示出了靶标的模型生成,其中靶标被校准板遮挡;Figure 3B shows model generation of a target, where the target is occluded by a calibration plate;
图4A示出了一组由边缘滤波器产生的靶标模型点;Figure 4A shows a set of target model points generated by an edge filter;
图4B示出了将靶标模型点细分成多个部分的示例细分,其中描述了模型中 心以及这些部分相对于模型中心的平移;Figure 4B shows an example subdivision of a target model point into parts depicting the center of the model and the translation of those parts relative to the center of the model;
图4C示出了典型的由于所述部分在附近的局部运动而产生的靶标模型的变 形;Figure 4C shows a typical deformation of the target model due to local movement of the parts in the vicinity;
图5示出了当前图像,该图像包含靶标的两个变形的实例以及就模型找到 的两个靶标检测结果实例;Figure 5 shows the current image containing two instances of deformations of the target and two instances of target detections found for the model;
图6示出了通过拟合变形函数而产生的刚性模板与变形模板之间的变形映 射,其中变形映射的示例点是部分的中心。Figure 6 shows the deformation map between the rigid template and the deformable template produced by fitting the deformation function, where the example point of the deformation map is the center of the part.
具体实施方式Detailed ways
下面结合附图对本发明作进一步描述。以下实施例仅用于更加清楚地说明 本发明的技术方案,而不能以此来限制本发明的保护范围。The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solutions of the present invention, but cannot limit the protection scope of the present invention with this.
本发明提供了一种用于可变形医学靶标识别的方法,其对遮挡、杂波以及 非线性对比度变化是鲁棒的。The present invention provides a method for deformable medical target recognition that is robust to occlusions, clutter, and nonlinear contrast changes.
图1给出了对所述方法的步骤的概括。提供了一种针对可变形医学靶标的 识别方法,包括以下步骤:Figure 1 gives an overview of the steps of the method. A method for identifying deformable medical targets is provided, comprising the steps of:
步骤1:通过相机获取医学靶标模型的图像;Step 1: Obtain the image of the medical target model through the camera;
步骤2:将上述获取得到的靶标的图像变换成与搜索空间的递归细分一致的 多级金字塔表示,所述多级金字塔中表示至少包括靶标模型的图像;Step 2: Transform the image of the target obtained above into a multi-level pyramid representation consistent with the recursive subdivision of the search space, wherein the multi-level pyramid representation includes at least an image of the target model;
步骤3:根据搜索空间的每一个离散化级,生成医学靶标的至少一个预先计 算的靶标模型,所述预先计算的靶标模型包括多个具有对应方向矢量的模型点, 所述靶标的模型点和方向矢量通过返回每一个模型点的方向矢量的图像处理操 作产生;Step 3: According to each discretization level of the search space, generate at least one pre-calculated target model of the medical target, the pre-calculated target model includes a plurality of model points with corresponding direction vectors, the model points of the target and Orientation vectors are generated by image processing operations that return an orientation vector for each model point;
步骤4:生成将所述多个模型点细分成多个部分的细分,其中,靶标模型的 变形实例通过对所述部分进行变换来表示;每一个部分由多个点组成,其中所 述细分生成交叠的点集。Step 4: Generate a subdivision that subdivides the plurality of model points into a plurality of parts, wherein the deformed instance of the target model is represented by transforming the parts; each part is composed of a plurality of points, wherein the Subdivision produces overlapping sets of points.
步骤5:通过相机获取搜索图像;Step 5: Obtain the search image through the camera;
步骤6:将搜索图像变换成与搜索空间的递归细分一致的多级金字塔表示, 所述多级金字塔表示至少包括所述搜索图像;Step 6: transforming the search image into a multi-level pyramid representation consistent with the recursive subdivision of the search space, the multi-level pyramid representation including at least the search image;
步骤7:对多级表示的每一个变换的图像执行图像处理操作,所述图像处理 操作返回所述搜索图像内的靶标模型点的子集的方向矢量,该子集对应于搜索 所述至少一个预先计算的靶标模型所应该针对的变换范围;Step 7: Perform an image processing operation on each transformed image of the multi-level representation that returns direction vectors for a subset of target model points within the search image corresponding to the search for the at least one The range of transformations that the precomputed target model should target;
步骤8:对于靶标模型点进行聚类,每个独立局部在一定范围内匹配计算得 到局部度量结果,通过计算由每个部分中的模型点数量归一化的结果总和获取 全局匹配度量,其中,对于局部度量,在接近于预先计算的靶标模型的受限制 的变换范围内搜索模型的部分,并将每个部分的最适合者视为该部分对全局匹 配度量的贡献;Step 8: Clustering the target model points, each independent part is matched within a certain range to obtain the local metric results, and the global matching metric is obtained by calculating the sum of the results normalized by the number of model points in each part, where, For local metrics, parts of the model are searched within a restricted range of transformations close to the precomputed target model, and the best fit for each part is considered as that part's contribution to the global matching metric;
步骤9:确定全局匹配度量超过了可选择的阈值且其匹配度量局部最大的靶 标模型姿态,以及,根据这些模型姿态产生处于搜索空间的最粗糙的离散化级 的所述至少一个预先计算的靶标模型的实例的列表;Step 9: Determining target model poses for which the global matching metric exceeds a selectable threshold and whose matching metric is locally maximum, and generating said at least one precomputed target at the coarsest discretization level of the search space from these model poses a list of instances of the model;
步骤10:通过获取每一个局部的最佳匹配获得了对变形的估算,计算描述 所述部分的局部位移的变形变换;Step 10: Obtained the estimate to deformation by obtaining the best match of each part, calculate the deformation transformation describing the local displacement of said part;
步骤11:通过搜索空间的递归细分,跟踪处于搜索空间的最粗糙的离散化 级的所述至少一个预先计算的靶标模型的实例,直到达到最精细的离散化级;Step 11: Track instances of said at least one pre-computed target model at the coarsest discretization level of the search space, until the finest discretization level is reached, by recursive subdivision of the search space;
步骤12:在每一级计算相应的变形变换,并将所述变形变换传送到下一级;Step 12: Computing the corresponding deformation transformation at each stage, and transmitting said deformation transformation to the next stage;
步骤13:在最精细的离散化级提供靶标模型实例的变形变换和模型姿态。Step 13: Provide the deformation transformation and model pose of the target model instance at the finest discretization level.
待识别的靶标模型包括多个具有对应方向矢量的点,这些点可以通过标准 图像处理算法来得到,例如通过边缘检测方法或线检测方法。在生成靶标模型 时,点集被分成多个部分。这些部分在搜索期间可相对于它们的原始位置移动, 从而允许模型灵活地改变它的形状。在一个优选实施例中,靶标模型的每一个 部分只包括一个模型点。在另一个优选实施例中,每一个部分包括若干邻近的 点,这些点相互之间保持刚性。The target model to be recognized includes a plurality of points with corresponding direction vectors, which can be obtained by standard image processing algorithms, for example by edge detection methods or line detection methods. When generating the target model, the point set is divided into multiple parts. These parts can be moved relative to their original positions during the search, allowing the model to change its shape flexibly. In a preferred embodiment, each part of the target model includes only one model point. In another preferred embodiment, each section comprises a number of adjacent points which are rigid relative to each other.
在搜索期间,例如针对广义仿射姿态范围将原始靶标模型实例化。在每一 个位置,模型的实例通过以近距离变换独立地变换每一个部分来变形。针对每 一个部分,在这个受限制的范围内的每一个变换处计算匹配度量。在一个优选 实施例中,匹配度量是该部分的方向矢量与经预处理的搜索图像的方向矢量的 归一化点积。整个靶标模型的匹配度量是最适合部分的变形变换处的最适合部 分的归一化总和。在一个优选实施例中,假定关于匹配度量的得分低于阈值的 那些部分处于遮挡状态,从而在进一步的处理中舍弃这些部分。匹配度量最大 的部分的变换决定该部分相对于原始位置的变形。这个位移被用来计算预先选 择的变形模型。在一个优选实施例中,非线性变形的模型是透视变换。在另一 个实施例中,它是例如样条函数或另一种本领域公知的用于对点集进行插值或 近似的方法。一旦计算出这个变换函数,可以反转找到图像区域的变形来产生 修正的图像。During the search, the original target model is instantiated eg for a generalized affine pose range. At each location, an instance of the model is deformed by transforming each part independently with a close-range transformation. For each part, a matching metric is computed at each transformation within this restricted range. In a preferred embodiment, the matching metric is the normalized dot product of the direction vector of the portion and the direction vector of the preprocessed search image. The matching metric for the entire target model is the normalized sum of the best-fit parts at the deformation transformations of the best-fit parts. In a preferred embodiment, those parts with a score below a threshold on the matching metric are assumed to be occluded and thus discarded in further processing. The transformation that matches the part with the largest metric determines the deformation of that part relative to the original position. This displacement is used to calculate the pre-selected deformation model. In a preferred embodiment, the model of nonlinear deformation is a perspective transformation. In another embodiment, it is, for example, a spline function or another method known in the art for interpolating or approximating a set of points. Once this transformation function has been computed, the deformation of the found image region can be inverted to produce a rectified image.
所述方法分成产生靶标模型的离线阶段和在搜索图像中找到所述靶标模型 的在线阶段。模型生成的输入是以未变形方式示出靶标的示例图像。在图2中, 示出了靶标形状的示例图像202。兴趣区域201限制靶标在图像中的位置。典型 地,这个区域由用户在离线训练阶段限定。如果靶标识别系统的用户只对随后 修正搜索图像中的靶标感兴趣,有时只需选取靶标的一部分作为感兴趣区域203。 其针对随后必须被解扭曲的模型而限定该区域的位置和尺寸。The method is divided into an offline phase of generating a target model and an online phase of finding said target model in a search image. The input to the model generation is an example image showing the target in an undistorted manner. In FIG. 2 , an
图像中的兴趣区域201仅指定了靶标在图像中的位置和尺寸。要确定靶标的 度量姿态,必须提供成像装置的内部几何参数。成像装置300(见图3A)的内部几 何参数典型地由以下几个方面进行描述:它的焦距、主点在图像中的位置、像 素元件在行和列方向的尺寸以及模拟由透镜引起的枕形畸变或桶形畸变的畸变 系数。相机的内部和外部参数可以通过本领域已知的方法事先确定(参见例如 MVTec Software GmbH,HALCON8.0Documentation,Solution Guide II-F,3d Machine Vision,2007)。The
一旦确定了这些参数,则需要靶标模型301的感兴趣区域203在相机坐标系 中的相对姿态来对靶标进行相对姿态估算(见图3和4)。因为通常没有成像靶标 的先验度量信息可供利用,所以不能说靶标是例如小且接近相机的或者是大且 远离相机的。这里的两种情况会得到相同的图像。提供这种度量信息的典型方 法是在一个优选实施例中,将已经测量的平面校准板303覆在医学靶标上,并 且获取显示校准板的图像302(见图3B)。在这个示意图中,校准板303包含暗圆 形的限定点。因为校准板的尺寸和点的精确度量位置是已知的,所以可以在相 机坐标系中确定校准平面的相对姿态。然后将校准板从靶标上移走,并获取显 示处与校准板相同的位置的靶标的第二幅图像。因为校准板的姿态和用于靶标 模型生成的姿态在世界和图像中是相同的,靶标的对应姿态得以自动确定。校 准板303的区域直接与用于模型生成的靶标的图像结合使用。Once these parameters are determined, the relative pose of the region of
医学靶标识别方法将模型生成的图像变换成包含原始图像的经平滑和子采 样的版本的递归细分。在以下表述中,递归细分、多级表示和图像金字塔在使 用时具有相同的含义。在一个优选实施例中,递归细分是均值图像金字塔。在 另一个优选实施例中,应用高斯图像金字塔。从限定靶标模型位置的兴趣区域 产生同一个多级表示。针对每一个多级表示,靶标模型生成从图像的所述区域 提取边缘点。边缘检测结果在图4A中示出。其中,边缘检测不仅提取位置,还 提取强对比度变化的方向。所使用的边缘检测是例如Sobel滤波或Canny边缘 检测滤波,或任何本领域已知的从图像提取有向特征点的其它滤波。本发明不限于边缘特征,本领域的技术人员能够容易地将其扩展到线特征或兴趣点特征。 为清楚起见,在进一步的讨论中我们只限于边缘点。图4A的小箭号400表示边 缘点的位置和方向。针对每一个模型点,所提取的边缘点被变换成模型坐标框 架(由圆圈401表示)并被保存到存储器中。因此,该识别方法得到对被成像靶标 的几何描述。Medical target recognition methods transform model-generated images into recursive subdivisions that contain smoothed and subsampled versions of the original images. In the following expressions, recursive subdivision, multi-level representation and image pyramid have the same meaning when used. In a preferred embodiment, the recursive subdivision is a mean image pyramid. In another preferred embodiment, a Gaussian image pyramid is applied. The same multilevel representation is generated from regions of interest defining the position of the target model. For each multi-level representation, a target model is generated to extract edge points from said region of the image. The edge detection results are shown in Figure 4A. Among them, edge detection not only extracts the position, but also extracts the direction of strong contrast changes. The edge detection used is for example a Sobel filter or a Canny edge detection filter, or any other filter known in the art for extracting directional feature points from an image. The present invention is not limited to edge features, and those skilled in the art can easily extend it to line features or interest point features. For clarity, we restrict ourselves to edge points in further discussion.
限定靶标模型原点的模型坐标框架401典型地通过获取点集的重心来计算。 坐标框架的取向与靶标相同。因此,将模型坐标框架映射成模板图像坐标框架 的变换是简单的平移。通过施加从模型坐标框架到图像坐标框架的广义仿射变 换映射,可以将模型的不同实例投射到图像中。A model coordinate
为考虑相继的非线性靶标模型变形,将所述多个边缘点组织成若干组的次多 个点。通过局部地变换所述若干组的次多个点,组与组相互之间的空间关系发 生变化,从而导致整个靶标的非线性形状变化。这里,施加于每一个组的局部 变换是足够小的仿射变换,或者是其子集,如刚性变换或平移。靶标的模型细 分在图4B中示出。部分生成的输入是事先通过特征提取产生的边缘点400的集 合。To account for successive nonlinear target model deformations, the plurality of edge points are organized into groups of sub-multiple points. By locally transforming the sub-multiple points of the groups, the spatial relationship of the groups to each other is changed, resulting in a non-linear shape change of the entire target. Here, the local transformations applied to each group are sufficiently small affine transformations, or a subset thereof, such as rigid transformations or translations. The model subdivision of the targets is shown in Figure 4B. The input to the partial generation is a set of edge points 400 previously generated by feature extraction.
一旦提取了边缘点,部分生成的任务是将这些点分组成空间相关的结构403。 这里,本发明假定空间相关的结构甚至在变换之后都保持相同。本发明的一个 方面是以人工方式进行聚类。这里,用户将会保持相似的部分选择成为一组。 本发明的另一个实施例通过自动方法执行聚类。一种直接的方法是对模型设置 固定的细分并将在一个细分单元内的点归属于一个部分。另一种方法是计算模 型点的邻域曲线图并将固定数量的最近的点选在一个部分内。要指出的重要的 一点是,本发明不限于这样的情况:即不同组的所述次多个点是分离的集合。 在一个优选实施例中,针对每一个点及其最近的邻近点生成一组次多个点。与 所使用的细分方法无关,模型点被分成n个部分,每个部分包含ki个模型点。 为了加速随后的计算,使用一种数据结构,对于每一个部分,该数据结构包含 它所包含的模型点的索引nij。这里,索引i的范围是从1到n,并限定哪一个部 分被选择;j从1到ki,并限定该部分的点。例如每一个部分具有相同数量的模 型点,则使用矩阵表示,其中每一行限定部分,每一列限定该部分中的索引。Once the edge points are extracted, part of the generative task is to group these points into a spatially correlated
限定这样一种细分之后,例如通过获取相应点集的重心来计算每一个部分 403的中心402。部分的中心与靶标模型401的原点之间的变换404保存在模型 中。因此,部分的中心的相对位置被转换成将模型的坐标框架变为部分的坐标 框架的变换,如欧几里德变换式。这些变换允许将模型点的位置和方向从部分 的坐标框架转换成模型的坐标框架的转换以及相反的转换。例如通过沿x和y 轴的小的移动或绕部分的中心的旋转来改变靶标模型与部分之间的相对变换 404,允许将靶标模型的变形版本实例化。由于沿x和y方向的小的平移而产生 的一些示例的变形在图4C中说明。After defining such a subdivision, the
本发明的一个方面是对已知的用于在存在部分遮挡、杂波以及非线性照度变 化的情况下检测图像中的医学靶标的方法加以扩展。One aspect of the present invention is to extend known methods for detecting medical targets in images in the presence of partial occlusions, clutter and non-linear illumination variations.
将靶标模型的有向点集与搜索图像的稠密梯度方向场相比较。即使传送到梯 度幅度的非线性照度变化相当大,梯度方向仍保持相同。此外,在搜索图像中 完全避免了滞后阈值或非最大抑制,从而实现了相对于任意照度变化的真正的 不变性。部分遮挡、噪声以及杂波导致搜索图像中的随机梯度方向。这些效应 降低了关于这个度量的得分的最大值,但不改变它的位置。得分值的语义是匹 配模型点的分数。Compare the directed point set of the target model with the dense gradient direction field of the search image. Even though the non-linear illuminance delivered to the gradient magnitude varies considerably, the gradient direction remains the same. Furthermore, hysteresis thresholding or non-maximum suppression is completely avoided in the search image, enabling true invariance with respect to arbitrary illumination changes. Partial occlusions, noise, and clutter lead to random gradient directions in the search image. These effects lower the maximum value of the score on this metric, but do not change its position. The semantics of the score value is the score of the matching model points.
有效搜索的思想是靶标识别仅对广义仿射变换或它的子集进行全局实例化。 通过允许部分的局部移动以及将最大响应作为最适合者,搜索隐含地对更高等 级的非线性变换进行了评估。在图5中对此进行了说明,其中示出了具有两个 变形靶标模型实例的搜索图像。在左边示出了模型的透射变换实例500。在右边 描述了更复杂的任意变形501。如图所示,局部适配的部分403在搜索图像中对 靶标进行近似。改变部分的刚性位置与局部适配的位置之间的局部变换,允许 表示很多种类的靶标模型外观。The idea behind efficient search is that target recognition is only globally instantiated for the generalized affine transformation or a subset of it. The search implicitly evaluates higher order non-linear transformations by allowing local movement of parts and taking the largest response as the best fit. This is illustrated in Figure 5, which shows a search image with two instances of the deformed target model. A
一项重要的观测结论是:通过将图像变换成金字塔表示,在每一级只需补偿 小的变形。例如,即使对象在最低的金字塔级具有复杂的变形,最高的金字塔 级处的外观也不发生大的变化。另一方面,如果靶标具有一个大的变形,则可 以在最高级对其进行补偿。在本发明中,变形以递归的方式沿着金字塔传送。 如果所有较高级别的变形都在较高的金字塔级得到补偿,则靶标的外观在每一 级只发生相对小的变化。An important observation is that by transforming the image into a pyramid representation, only small deformations need to be compensated at each level. For example, even though an object has complex deformations at the lowest pyramid level, the appearance at the highest pyramid level does not change much. On the other hand, if the target has a large deformation, it can be compensated at the highest level. In the present invention, deformations are propagated along the pyramid in a recursive manner. If all higher-level deformations are compensated at higher pyramid levels, the appearance of the target changes relatively little at each level.
因此,通过将搜索度量分成全局部分sg和局部部分sl。为清楚起见,我们只 给出用于平移的公式,这意味着只针对每一行r和列c计算得分。可以直接将其 扩展为用于广义仿射参数。如上所述,靶标模型被分成n部分,每一部分包含 ki个模型点。Therefore, by splitting the search metric into a global part s g and a local part s l . For clarity, we only give the formula for translation, which means that the score is only calculated for each row r and column c. It can be directly extended for generalized affine parameters. As mentioned above, the target model is divided into n parts, each part contains ki model points.
全局度量定义为:The global metric is defined as:
意思是:它是针对索引i所限定的每一个部分计算的局部匹配的得分值的组 合。Meaning: it is the combination of score values of partial matches calculated for each part defined by index i.
局部匹配度量定义为:The local matching metric is defined as:
这里,ij对限定指示靶标中哪一个模型点在哪一部分中的索引,其中每一部 分具有ki个模型点。rij和cij是相应的模型点在模型坐标系中的行和列位移。局 部变换Tl用来改变靶标模型的形状。典型地,这些是具有例如在每一个方向上1 个像素平移的小作用的欧几里德变换。上标m和s限定d是靶标模型的方向矢 量或搜索图像中的相应位置的方向矢量。Here, the ij pairs define an index indicating which model point in the target is in which part, where each part has ki model points. r ij and c ij are the row and column displacements of the corresponding model points in the model coordinate system. The local transformation Tl is used to change the shape of the target model. Typically these are Euclidean transforms with small effects such as 1 pixel translation in each direction. The superscripts m and s define d to be the direction vector of the target model or the corresponding position in the search image.
在每一个可能姿态位置,每一个部分具有如独立的得分值,针对每一个部分, 在其原始仿射位置附近的范围内评估所述度量。局部邻域内的最大得分作为该 部分的最适合者。通过计算由每一个部分中的模型点的数量归一化的局部度量 的结果的总和来获取全局度量。本发明的一个变体是可以针对每一个部分设置 一个该部分所必须超过的阈值。否则,认为所述部分被遮挡,因此将其舍弃而 不做进一步的处理。At each possible pose position, each part has a separate score value, for which the measure is evaluated in a range around its original affine position. The maximum score within the local neighborhood is taken as the best fit for that part. The global metric is obtained by summing the results of the local metrics normalized by the number of model points in each part. A variant of the invention is that a threshold can be set for each section that the section must exceed. Otherwise, the part is considered occluded, so it is discarded without further processing.
其中 in
另一个优选实施例是当部分的尺寸不同时。此时,人们通过每一个部分所包 含的靶标模型点的数量来对其影响进行加权。Another preferred embodiment is when the parts are of different sizes. At this point, one weights the impact of each part by the number of target model points it contains.
在不知道确切的变形时,一组广义仿射变换的全局得分值也允许确定靶标近 似的位置。另一个变体是:为了实现相对于对比度反转的不变性,从局部度量 结果中舍弃极性信息。这是通过在局部度量中使用模型点和图像点的方向矢量 的归一化点积的和的绝对值或绝对值的和来完成的。A set of global score values for generalized affine transformations also allows determining the approximate position of the target when the exact deformation is not known. Another variant is to discard polarity information from local metric results in order to achieve invariance against contrast inversion. This is done by using the absolute value or the sum of absolute values of the sum of the normalized dot products of the direction vectors of the model and image points in the local metric.
通过获取每一个部分的最佳匹配,不仅获得了得分值,还获得了对变形的估 算。这些是限定最大局部得分的局部变换Tl。在具有了每一个部分的局部位移 之后,相应的非线性靶标模型被拟合。即使对于没有模型点的位置,也可以计 算平滑变形。图6示出了一个示例变形。部分402的中心移动到附近的位置603。 针对这些点来拟合非线性变换,将原始刚性空间(示意性地描述为网格601)变换 成变形空间602。在本领域这是一个众知的问题,已经提出了根据函数插值和近 似来实现的各种解决方案。这里,本发明的一个方面是只将每一个部分的局部 位移用作函数点并为每一个点拟合例如透视变换。对于可变形的靶标模型,样 条函数由位移来定义。这个样条函数是例如B样条函数或薄板样条函数。这些 函数的系数通过直接方法计算。然而,如果使用了例如薄板样条函数,则必须 反转很大的线性系统(liner system)来获取扭曲系数。因此,在另一个优选实施例 中使用由模型点的变形限定的调和插值方法。此时,将模型点的位移插入描述 沿行和列方向扭曲的两个图像中。然后,通过被称为调和修补的方法为没有模 型点的区域修补变形。为了使扭曲平滑,将变形传回到模型点的原始区域。因 此,不仅获得了插值函数,还获得了近似函数。这个方法的优点是:运行时间 只是线性相关于靶标的尺寸,而不是例如像薄板样条那样三次方地相关于锚点 的数量。By obtaining the best match for each part, not only the score but also an estimate of the deformation is obtained. These are the local transformations T l defining the maximum local score. After having the local displacements of each segment, the corresponding nonlinear target model is fitted. Smooth deformations can be computed even for locations where there are no model points. Figure 6 shows an example variant. The center of
通常,特别是对于严重变形,不可能一步提取到变形。当给定变形映射时, 对全部模型点和相应的方向进行变换。利用这个被变换的靶标模型,现在针对 局部位移再一次独立地搜索模型的每一组次多个点。这给出了确定小位移并拟 合被评估的模型的循环,直到达到收敛。典型地,通过检查位移是否变得小于 预定阈值来检验收敛。对于所限定的超过阈值并且局部最大的全局实例的范围, 将具有位置、得分和变形信息的靶标假设放入列表,以便在较低的金字塔级对 它们作进一步的检查。在一个优选实施例中,不仅设置关于全局得分值的阈值, 而且还设置产生于最高金字塔级的假设的最大数目。此时,全部假设根据它们 的得分值而分类,而且只有固定数量的最佳匹配的候选者才被放入进一步处理的假设的列表中。Often, especially for severe deformations, it is impossible to extract the deformations in one step. When a deformation map is given, all model points and corresponding orientations are transformed. Using this transformed target model, each set of submultiple points of the model is now again independently searched for local displacements. This gives a loop of determining small displacements and fitting the model being evaluated until convergence is reached. Typically, convergence is checked by checking whether the displacement becomes smaller than a predetermined threshold. For the defined range of global instances that exceed a threshold and are locally maximal, target hypotheses with position, score, and deformation information are put into a list so that they can be further checked at lower pyramid levels. In a preferred embodiment, not only the threshold on the global score value is set, but also the maximum number of hypotheses generated from the highest pyramid level. At this point, all hypotheses are sorted according to their score values, and only a fixed number of best matching candidates are put into the list of hypotheses for further processing.
一旦确定了靶标模型在特定金字塔级的确切位置和变形,必须沿着金字塔将 该变形传送到下一个金字塔级。这样做是重要的,以便在较低的级仅仅必须评 估小搜索范围的局部变形。在一个优选实施例中,来自较低级的原始仿射模型 通过递归细分而变换成较高的金字塔级。已经提取的较高级的变形被施加到模 型上,而现在已变换的来自较低级的模型被变回到它的原始金字塔级。在这个 级上的搜索从根据较高金字塔级的变形变换的模型的实例开始。Once the exact position and deformation of the target model at a particular pyramid level has been determined, that deformation must be passed along the pyramid to the next pyramid level. This is important so that at lower levels only local deformations of a small search range have to be evaluated. In a preferred embodiment, the original affine models from lower levels are transformed into higher pyramid levels by recursive subdivision. The higher level deformations that have been extracted are applied to the model, while the now transformed model from the lower level is transformed back to its original pyramid level. The search at this level starts with an instance of the model transformed according to the deformation of the higher pyramid level.
沿着图像金字塔对假设的跟踪一直进行到达到最低的金字塔级为止。在最低 的金字塔级,以甚至高于原始图像的分辨率来确定位移。因此,在亚像素精度 的位置对部分进行实例化,并且确定图像中的对应的最大边缘幅度。此时,部 分的位移不再由梯度方向限定,而由梯度幅度限定。按照以上方法,使用小位 移以很高的精度来拟合变形函数。一旦在最低级找到对象,则返回位置、姿态 和变形函数。此外,返回全局得分函数的值,以便为用户提供更好的程度找到 靶标的量度。Hypotheses are tracked along the image pyramid until the lowest pyramid level is reached. At the lowest pyramid level, the displacement is determined at an even higher resolution than the original image. Accordingly, sections are instantiated at sub-pixel precision locations and the corresponding maximum edge magnitudes in the image are determined. At this point, the partial displacement is no longer limited by the direction of the gradient, but by the magnitude of the gradient. Following the above method, the deformation function is fitted with high accuracy using small displacements. Once the object is found at the lowest level, the position, pose and deformation functions are returned. Additionally, the value of the global scoring function is returned to provide the user with a better measure of how well the target was found.
实施例二:Embodiment two:
本实施例提供一种针对可变形医学靶标的识别装置,包括存储在计算机可 读介质上的程序代码装置,用于在所述计算机程序产品在计算机上运行时执行 实施例一所述的针对可变形医学靶标的识别方法。This embodiment provides an identification device for a deformable medical target, which includes a program code device stored on a computer-readable medium, and is used to execute the method for deformable medical targets described in the first embodiment when the computer program product is run on a computer. Identification methods for deformable medical targets.
本领域内的技术人员应明白,本申请的实施例可提供为方法、系统、或计 算机程序产品。因此,本申请可采用完全硬件实施例、完全软件实施例、或结 合软件和硬件方面的实施例的形式。而且,本申请可采用在一个或多个其中包 含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、 CD-ROM、光学存储器等)上实施的计算机程序产品的形式。Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品 的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/ 或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或 方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式 处理机或其他可编程数据处理设备的处理器以产生一个机器,使得通过计算机 或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流 程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It should be understood that each process and/or block in the flowchart and/or block diagrams, and a combination of processes and/or blocks in the flowchart and/or block diagrams can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备 以特定方式工作的计算机可读存储器中,使得存储在该计算机可读存储器中的 指令产生包括指令装置的制造品,该指令装置实现在流程图一个流程或多个流 程和/或方框图一个方框或多个方框中指定的功能。These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上,使 得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理, 从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程 或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, whereby the The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.
以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通 技术人员来说,在不脱离本发明技术原理的前提下,还可以做出若干改进和变 形,这些改进和变形也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, some improvements and modifications can also be made. It should also be regarded as the protection scope of the present invention.
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