Description: 使用图像的互信息进行自动图像配准源代码，这个程序通过归一化后的图像互信息进行图像的自动配准。中间通过旋转等进行图像对齐。-The use of images for automatic mutual information image registration source code, the procedure adopted by the normalized mutual information image after image of the automatic registration. Through the rotation between image alignment. Platform: |
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Author:yy |
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Description: The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented. Platform: |
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Author:Harry |
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Description: 图像配准中的两幅图像的归一化互信息计算方法代码-Image registration of two images of the normalized mutual information method code Platform: |
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Author:王丽 |
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Description: 图像配准在医学图像处理领域是一项重要的技术，对临床诊断和治疗起着越来越重要的作用。尽管对
这方面的研究已经展开多年，但目前的主要方法仍然存在不足之处，急需改进，以便更好的应用于临床实践。
本文主要针对现在流行的基于最大互信息量的配准方法展开讨论和研究。在此基础上提出了相同重叠区域下的配准框架，在此框架下，将一些统计相似性配准算法统一为基于最小条件熵的图像配准算法。通常称为归一化互信息配准的方法。-Image registration in medical image processing is an important technique for clinical diagnosis and treatment plays an increasingly important role. Although research in this area has started for many years, but there are still the main method of the inadequacies of the urgent need to improve, in order to better clinical practice. In this paper, for now the most popular mutual information based registration method to discuss and research. On this basis, the same overlap region proposed registration under the framework, in this framework, some of the statistical similarity matching algorithm uniform minimum conditional entropy-based image registration algorithm. Often referred to as normalized mutual information registration method. Platform: |
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Author:xingxing |
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Description: 本文提出了一种基于图像配准的自动目标识别算法,图像配准算法采用基于归一化互信息相似性判据,并采用模糊自适应粒子群优化算法作为搜索策略。在图像精确配准的基础上,通过图像间的相互转换,间接实现了目标的准确识别。仿真试验结果表明,该方法可以实现复杂背景下目标的准确识别。
-This paper presents a novel image registration algorithm for automatic target recognition, image registration algorithm based on normalized mutual information similarity criterion, and the fuzzy adaptive particle swarm optimization algorithm as search strategy. In the image on the basis of accurate registration, through the conversion between images, the accuracy of indirect recognition to achieve the target. Simulation results show that the complex background can accurately identify targets. Platform: |
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Author:wenping |
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Description: 此程序可以实现对图像进行配准，所采用的相似性测度包括归一化互信息，互信息等多种测度，同时包含多种测试程序。-This procedure can be achieved for the image registration, using the similarity measure of the mutual information, normalized mutual information, and many other measure, contain both a variety of testing procedures. Platform: |
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Author:陈乐 |
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Description: In this paper, we propose a novel registration
algorithm based on minimal spanning tree. First, we extracted
uniform sub-sampling points from image. Second, based on the
feature points, in addition to using pixel intensity, we also added
region based feature to include more spatial information. The
proposed method is evaluated by performing registration
experiments on BrainWeb database. The experimental results
show that the proposed method achieves better robustness while
maintaining good registration accuracy, compared to the
conventional normalized mutual information (NMI) based
registration method. Platform: |
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Author:Salkoum |
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Description: Investigatingmulti-feature information-theoretic image registration,
we introduce consistent and asymptotically unbiased
kth-nearest neighbor (kNN) estimators of mutual information
(MI), normalized MI and exclusive information applicable to
high-dimensional random variables, and derive under closedform
their gradient flows over finite- and infinite-dimensional
transform spaces. Using these results, we devise a novel unsupervisedmethod
for the groupwise registration of cardiac perfusionMRI
exams. Here, local time-intensity curves are used
as a dense set of spatio-temporal features, and statistically
matched through variational optimization. Experiments on
simulated and real datasets suggest the accuracy of the model
for the affine registration of exams with up to 34 frames. Platform: |
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Author:Salkoum |
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Description: In this paper, we propose a novel medical
registration approach based on minimal spanning tree. The
proposed approach has the following contributions. (1)
Compared with single type of feature points, we extracted
corner-like and edge-like points from image, and added a few
random points to cover the low contrast regions. (2) Instead of
fixing the multi-feature points in the whole procedure, they are
hierarchically updated at different registration stages. (3)
Based on the feature points, in addition to using pixel intensity,
we also added region based feature to include more spatial
information. The proposed method is evaluated by performing
registration experiments on BrainWeb. The experimental
results show that the proposed method achieves better
robustness while maintaining good registration accuracy,
compared to the conventional normalized mutual information
(NMI) based registration method Platform: |
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Author:Salkoum |
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Description: 通过matlab实现：通过计算归一化互信息进行判断实现图像配准。-Through matlab: by calculating the normalized mutual information to judge to realize image registration. Platform: |
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Author:zhq |
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Description: 进行逐步线性回归，计算互信息非常有用的一组程序,数据模型归一化，模态振动。- Stepwise linear regression, Mutual information is useful to calculate a set of procedures, Normalized data model, modal vibration. Platform: |
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Author:kaiqei |
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Description: Normalized data model, modal vibration, Mutual information is useful to calculate a set of procedures, LCMV optimization design array signal processing. Platform: |
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Author:lingsaijanyou
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Description: 1) 使用凝聚型层次聚类算法（即最小生成树算法）对所有数据点进行聚类，最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。
2) 使用C-Means算法对所有数据点进行聚类。C=3。
任务2（必做）：
使用高斯混合模型（GMM）聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数（包括比例??、均值??和协方差Σ）。
任务3（全做）：
1) 参考数据文件第三列的类标签，使用聚类有效性评价的外部方法Normalized Mutual Information指标，分别计算任务1和任务2聚类结果的有效性。
2) 使用聚类有效性评价的内部方法Xie-Beni指标，分别计算任务1和任务2聚类结果的有效性。(The main results are as follows: 1) the condensed hierarchical clustering algorithm (that is, the minimum spanning tree algorithm) is used to cluster all the data points, and finally it is grouped into three categories. Any of the single linkage,complete linkage,average linkage or average group linkage methods can be selected for the definition of dissimilarity. 2) using C-Means algorithm to cluster all data points. C = 3.) Platform: |
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Author:小鹏鹏123 |
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Search - normalized mutual information