共查询到18条相似文献,搜索用时 109 毫秒
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针对多模医学图像配准的高精度要求,以最大化互信息图像配准方法为基础,提出了一种基于混合量子行为的粒子群优化(HQPSO)算法的多模医学图像配准新方法。实验结果表明,在多模医学图像配准应用中,新算法的实际性能不仅优于传统的Powell算法和PSO算法,也比QPSO算法有一定的优势。上述结论为医疗图像诊断分析提供了一种新的有效方法。 相似文献
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角点含有丰富的图像结构信息,在图像配准中是广泛应用的图像特征。Harris算法是经典的角点提取算法,Harris角点对图像旋转具有不变性,但对尺度变化敏感,在有尺度变化的图像配准中,应用受限。仿照SIFT特征点提取过程,提出了一种多尺度角点提取方法,提取的多尺度角点对图像旋转和尺度变化有很好的适用性。并用SIFT描述子描述,用光学及SAR图像进行了配准实验。结果表明,与SIFT、Harris算法相比,本文方法在保证配准精度的基础上,配准时间减少40%以上,特征点在配准过程中的利用率提高一倍多。 相似文献
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医学图像配准是医学图像分析诊断的基础,也是图像融合等图像处理需要先行解决的问题。首先用Canny算子提取图像的边缘,再用K-Means聚类算法进行聚类分析提取轮廓特征点,然后提出了一种改进的粒子群优化(IPSO)算法来求解配准所需的空间变换参数。实验结果表明:改进PSO能够迅速地在全局范围内找到最优解,应用于多模态医学图像配准是可行的。 相似文献
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改进的RANSAC算法在图像配准中的应用 总被引:8,自引:1,他引:7
为了提高图像配准的速度,提出了一种基于改进的随机抽样一致性(RANSAC)算法的快速图像配准方法。该方法首先采用Harris角点检测算法提取出参考图像和目标图像的特征角点,然后利用灰度相关性进行特征角点的匹配,最后采用基于预检测的RANSAC算法快速而精确地估计变换矩阵,进行图像配准。该算法中采用预检测的方法快速抛弃那些不是候选模型的临时模型,提高了算法的速度。同时使用随机块选取法选择样本,很好地消除外点的影响进而保证精度。实验结果表明,此方法在得到较高的精度和鲁棒性的情况下,还大幅度减少了运算量,提高了图像配准的速度。 相似文献
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为满足医学图像配准对多分辨率,高配准率,低时耗率的高要求,提出了一种新颖的基于多尺度Harris角点方根-算术均值距离(SAM)的配准算法。该算法通过对图像进行小波多尺度积边缘检测和多尺度Harris角点检测,首先得到了估计变换参数;然后利用角点间的SAM作为相似性测度函数来获得最佳匹配点对,并通过最小二乘得到最终配准参数。实验表明,算法可实现含噪声图像以及不同分辨率的多模医学图像的配准,由于算法只对角点匹配,无须最优搜索,从而不仅大大减少了计算量,而且避免了陷入局部极值的情况。最后,通过3类实验验证了算法的可行性和鲁棒性。 相似文献
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针对图像配准问题,提出了基于Harris及SIFT(Scale-invariant feature transform)特征的Hausdorff距离方法来实现图像配准。首先利用harris角点检测和SIFT特征提取参考图像和待配准图像的角点,通过两种方法获得的角点在融合之后获得更大的角点搜索范围,再利用相似一致性匹配原则剔除错误角点,进而通过改进的Hausdorff距离算法完成图像的配准操作。结果证明,改进算法比传统Hausdorff距离算法运行时间更短,算法时间降低约45%,具有较强的抗噪声能力和旋转鲁棒性,提高了图像配准的效率和精确性。 相似文献
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《软件》2018,(1):75-82
ICP算法广泛应用于医学图像配准,但存在浮动点集初始平移矩阵和旋转矩阵对ICP的影响较大,图像配准容易造成目标函数陷入局部最优值且计算量大等问题。论文提出了基于改进K-Means聚类医学图像配准算法,该方法通过计算出参考图像和浮动图像的质心,获得配准平移初始值;对医学图像坐标进行中心化处理,通过改进的K-Means聚类方法把图像坐标聚成2类;把这2个聚类中心拟合成一条直线,求得该条直线的斜率,进而求得相关倾斜角,获得配准旋转初始值;使用BSGO自动选择特征点,得到参考点集和浮动点集。通过实验得出该算法既可用于单模态图像配准,也可用于多模态图像配准;具有运算量少、图像配准速度较快、计算比较简单、精确度较高等特点,并且解决了图像配准容易陷入局部最优的问题。 相似文献
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基于最大互信息和量子粒子群优化算法的医学图像配准研究* 总被引:5,自引:1,他引:4
研究了基于最大互信息的图像配准算法,在图像配准中引入了新的相似性测度,在分析具有量子行为的粒子群优化算法基础上,将量子粒子群算法作为优化策略用于图像配准并与Powell算法和PSO算法进行了仿真比较,对仿真结果进行了分析。 相似文献
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角点检测算法是基于角特征点的图像配准方法的核心。Harris和Susan是两种重要的角点检测算法,有较好的检测能力,但是其在描述角点信息方面都不全面。因此,联合Harris、Susan两种算法是一种较好的解决思路。其中,如何确定在联合算法中Harris、Susan两种算法的权重是一个关键。设计了一种联合算法,并通过统计实验获取两者的权重,通过引入两个加权因子ω1和ω2分别对Harris角点响应值与Susan角点响应值进行加权计算,获得其角点强度,从而筛选出新的角点集合,使该联合算法的角点检测能力明显提高。最后将该方法用于脑磁共振图像配准实验中。实验比较结果表明,该联合角点检测算法在脑磁共振图像配准的应用中,相对于目前已有角点检测算法,能获得较高的配准精度和较好的稳定性。 相似文献
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Dinggang Shen Author Vitae 《Pattern recognition》2007,40(4):1161-1172
We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images. 相似文献
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Ravichandran A Vidal R 《IEEE transactions on pattern analysis and machine intelligence》2011,33(1):158-171
We consider the problem of spatially and temporally registering multiple video sequences of dynamical scenes which contain, but are not limited to, nonrigid objects such as fireworks, flags fluttering in the wind, etc., taken from different vantage points. This problem is extremely challenging due to the presence of complex variations in the appearance of such dynamic scenes. In this paper, we propose a simple algorithm for matching such complex scenes. Our algorithm does not require the cameras to be synchronized, and is not based on frame-by-frame or volume-by-volume registration. Instead, we model each video as the output of a linear dynamical system and transform the task of registering the video sequences to that of registering the parameters of the corresponding dynamical models. As these parameters are not uniquely defined, one cannot directly compare them to perform registration. We resolve these ambiguities by jointly identifying the parameters from multiple video sequences, and converting the identified parameters to a canonical form. This reduces the video registration problem to a multiple image registration problem, which can be efficiently solved using existing image matching techniques. We test our algorithm on a wide variety of challenging video sequences and show that it matches the performance of significantly more computationally expensive existing methods. 相似文献
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Image registration is a crucial progress in detecting oil spilled on the sea and is also important for estimating the volume of the oil spill, especially when one image cannot cover the entire polluted region. In this article, a new algorithm is proposed to register geometrically distorted aerial images of oil spill accurately and automatically. There are two stages in this algorithm: coarse registration and fine registration. Invariants-based similarity and relative space distance are applied to coarse matching. Then improved iterative closest point (ICP) algorithm is used for registering images finely, which is the combination of ICP and a method of solving assignment problem to deal with mismatches. The performance of the proposed algorithm is evaluated by registering oil spill ultraviolet (UV) and infrared (IR) images, respectively. Compared with traditional ICP and other algorithms, the efficiency and accuracy of the proposed algorithm are highly improved. 相似文献
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多源遥感数据的融合和综合应用必须实行严格的配准,若将通过选取控制点的传统方法,用于成象特性差异较大的图象间配准就存在较大的误差,为解决该问题,研究发展了一种基于分窗口相关的图象配准方法,即采用移动窗灰度相关的方法对图象上的每一点进行搜索,来寻找最大相关位置,以达到精确配准的目的。通过将该方法应用于不同时相的TM图象、SAR图象、不同成象方式和不同分辨率的AVIRIS图象和航片间配准的实验表明,该方法能够有效地实现复杂图象间的精确配准,配准误差已达到子象素级水平。 相似文献
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研究基于点特征的图像配准方法。首先利用canny算子提取图像的边缘,然后用MIC角点检测算子提取边缘中的角点,对提取出的角点进行配对后,利用仿射变换实现图像的配准。最后以脑图像配准验证了算法的有效性。 相似文献
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Iris recognition is one of the most reliable personal identification methods. In iris recognition systems, image registration is an important component. Accurately registering iris images leads to higher recognition rate for an iris recognition system. This paper proposes a phase correlation based method for iris image registration with sub-pixel accuracy. Compared with existing methods, it is insensitive to image intensity and can compensate to a certain extent the non-linear iris deformation caused by pupil movement. Experimental results show that the proposed algorithm has an encouraging performance. 相似文献