Description: 利用MATLAB编辑的医学图像配准程序，可以把两幅相关图象进行配准-using MATLAB editors of medical image registration procedures can be two related images registration Platform: |
Size: 169206 |
Author:肖昕璐 |
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Description: 医学影像配准（Medical Image Registration）的入门资料。其中包括：
Fast Parametric Elastic Image Registration.pdf
Image Registration of Sectioned Brains.pdf
Mutual-information-based registration of medical images - a survey.pdf
Registration of histological serial sectionings.pdf
RegistrationMethodsOverview.pdf Platform: |
Size: 11051367 |
Author:ricky yan |
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Description: 利用MATLAB编辑的医学图像配准程序，可以把两幅相关图象进行配准-using MATLAB editors of medical image registration procedures can be two related images registration Platform: |
Size: 168960 |
Author:肖昕璐 |
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Description: 医学影像配准（Medical Image Registration）的入门资料。其中包括：
Fast Parametric Elastic Image Registration.pdf
Image Registration of Sectioned Brains.pdf
Mutual-information-based registration of medical images - a survey.pdf
Registration of histological serial sectionings.pdf
RegistrationMethodsOverview.pdf-Medical Image Registration (Medical Image Registration) introductory information. These include: Fast Parametric Elastic Image Registration.pdfImage Registration of Sectioned Brains.pdfMutual-information-based registration of medical images- a survey.pdfRegistration of histological serial sectionings.pdfRegistrationMethodsOverview.pdf Platform: |
Size: 11051008 |
Author:ricky yan |
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Description: 用来做医疗图像三维重建、二维分割和配准的库开发包，很好用，只需要把相关的类找到，建立一个pipeline即可，功能很强大的。-Used to make three-dimensional reconstruction of medical images, two-dimensional segmentation and registration of library development kit, very good use, just to find a related category, the establishment of a pipeline can be functional very powerful. Platform: |
Size: 6072320 |
Author:张培培 |
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Description: 本方法是对二维医学图像实现的Walsh变换，属于预处理范围，为下一步的图像分割，配准等打下基础-This method is the realization of two-dimensional medical images of the Walsh transform, belong to the scope of pre-treatment, for the next phase of image segmentation, registration, etc. to lay the groundwork Platform: |
Size: 2955264 |
Author:hujin |
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Description: 基于边缘特征的图像配准算法源码
基于边缘特征的图像配准算法是将不同时间、不同的传感器（成像设备）或不同条件下（天候、照度、摄像位置和角度等）获取的同一场景的两幅或多幅图像根据其边缘特征进行匹配、叠加的处理，最终生成一幅全景图像的方法。该方法具有抗噪性强，匹配速度快，误匹配率低，适用于多种类型的图像等优点，主要可以运用于以下领域：
（1）军事研究领域，如飞行器辅助导航系绞、武器投射系统的末制导以及寻地等应用研究；
（2）医学图像分析，如数字剪影血管造影DSA血管造影术、肿瘤检测、白内障检测、CT、MRI、 PET、 SPECT图像结构信息融合；
（3）模式识别，如特征识别、波形分析、签名证实、目标物运动跟踪、序列图像分析等；
（4）计算机视觉，可用于目标定位、自动质量控制等；
（5）遥感数据分析，多电磁波段图像信息融合，如多谱段的等场景分类、定位和识别定义好的或已知特征的场景、自然资源监控、核生长监控、市区增长检测等。
本算法实现使用VC++6.0为开发环境，目前只支持灰度图像，感兴趣的朋友可以进一步扩展。-Image registration is a fundamental problem in image processing, which is a process to match two or more images of the same scene taken at different times, from different viewpoints, or by different sensors. Image registration is an important technique for a great variety of applications such as medical image analysis, computer vision, remote image data analysis, image information fusion, and the recovery of the 3–D characteristics of a scene. Platform: |
Size: 141312 |
Author:YYK |
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Description: 这是一个3D医学图像处理软件，可以对图像进行分割和配准，对于三维和二维的医学图像都可以使用。-This is a 3D medical image processing software, you can image segmentation and registration for three-dimensional and two-dimensional medical images can be used. Platform: |
Size: 24842240 |
Author:gaojuan |
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Description: 医学图像图像配准方法，采用了经典的基于灰度的方法。-This is a general purpose registration method for medical images. Platform: |
Size: 2600960 |
Author:feelfree |
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Description: 2-D and 3-D
Image Registration
for Medical, Remote Sensing,
and Industrial Applications
Image registration is the process of determining the point-by-point correspondence
between two images of a scene. By registering two images, the fusion ofmultimodality
information becomes possible, the depth map of the scene can be determined,
changes in the scene can be detected, and objects can be recognized. Platform: |
Size: 4958208 |
Author:lily |
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Description: 数字影像重建在基于灰度的2D／3D医学图像配准技术中具有重要作用。它是利用基于射线追踪算法从三维数据如CT中获得二维虚拟x线图像。影像重建结果的质量直接影响到配准结果的精度。本文对数字影像重建技术作了一定的探讨，实现了对cT体数据的数字影像重建，并通过增强技术获得了突出骨组织的图像，为后续的配准研究做了很好的准备工作。-Digital image reconstruction in intensity based 2D/3D medical image registration techniques have an important role. It is the use of ray-tracing algorithm based on the data from the three-dimensional CT obtained as two-dimensional virtual x-ray images. Image reconstruction quality of the results directly affect the accuracy of registration results. In this paper, digital image reconstruction techniques were explored certain to achieve volume data on the number of cT image reconstruction, and by enhancing technology to obtain a prominent image of bone tissue, for follow-up studies with prospective done a very good preparations. Platform: |
Size: 106496 |
Author:liaozw |
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Description: 用于检测3D血管图像的分叉点的一个程序，文件夹内还有说明文档，详细介绍了该程序的算法。是用itk库做的一个通用模板类，还有例子。- Branches of tubular structures (vasculature, trachea, neuron, etc.) in medical images are critical for the topology of these
structures. In many applications, It is very helpful to be able to decompose tubular structures and identify every individual
branch. For example, quantification of geometric vascular features, registration of trachea movement due to respiration, tracing of
neuron path. However, manual decomposition can be tedious, time-consuming, and subject to operator bias. In this paper, we
propose a novel method to decompose tubular structures automatically and describe how to implement it in ITK framework. The
input is a 2D/3D binary image that can be obtained from any segmentation techniques, as well as the junctions, which can be
generated automatically from our previously contributed ITK class: itk::JunctionDetectionFilter. The output will be
branches with their labels and their connection. There are only tw Platform: |
Size: 2524160 |
Author:xrx |
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Description: 基于MFC的医院挂号系统，采用Orecal数据库来进行医生图片的大字段存储。能实现挂号、退号、医生管理、登陆等问题。便于VC初学者和Orecal数据库的初学者学习，简单易懂-MFC-based hospital registration system, using Orecal database for medical images stored in the large field. To achieve registration, back number, the doctor management, land and other issues. Easy for beginners and Orecal VC database beginners to learn, easy to understand Platform: |
Size: 75776 |
Author:刘伟 |
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Description: 医学图像配准中的刚性配准两幅图像，希望大家用得到。-Medical Image Registration in the rigid registration of two images, I hope you get to use. Platform: |
Size: 175104 |
Author:毛毛hhmm |
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Description: 粒子群优化算法的代码，也可以实现对脑部医学图像的刚性配准。-Particle swarm optimization algorithm code can also be rigid registration of medical images of the brain. Platform: |
Size: 2048 |
Author:赵小川 |
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Description: 提出了利用小波分解建立多分辨率图像锥和Hausdorff距离的医学图像配准方法。先利用小波方法建立多分辨率图像锥, 然后根据梯度向量幅度提取分层图像的特征点, 利用Hausdorff距离进行特征点集的匹配。该方法提高了配准的速度和精度, 而且具有鲁棒性。-Image registration is the matching processing in which two or more images match from the same scene derived from different time and different sensors. A new method of medical image registration based on multi-resolution image cone and Hausdorff Distance is put forward. Firstly, the image is decomposed with wavelet method and transformed to multiresolution image. Multiresolution image cone is constructed by multiresolution image. Secondly, gradient vector flow is used to detect feature points. Finally, Hausdorff Distance is used for matching sets of feature points. Theories and experiments indicate that this method has some advantages such as high precision and good robustness. Platform: |
Size: 300032 |
Author:vivi |
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Description: 研究了基于互信息测度的医学图像配准方法,提出了一种优化算法的改进。目的旨在于解决配准的精度和在基于互信息配准过程中的效率问题。提出的优化算法是将拟牛顿方法运用于多模医学图像配准中。实验结果说明这种改进的方法能有效提高配准的精度和效率问题,并得到好的实验效果。-Abstract: This paper presents a novel Optimized method for medical image registration, the purpose is to solve problems, which are the registration precision and huge calculation in medical images registration based on mutual information. We present a novel optimized method, called Quasi-Newton (BFGS algorithm), for multi-modal medical image registration. Experimental results show that the method, which develops precision of registration, can solve the problem of huge calculation, and achieve good effects. Platform: |
Size: 1452032 |
Author:vivi |
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Description: 【摘要】 基于互信息的配准方法是医学图像配准领域的重要方法。互信息是图像配准中常用的相似性度量,具有鲁棒、精度高等优点,但基于互信息的配准计算量大,制约了它的实际应用。我们采用基于多分辨率和混合优化策略的配准方法,在图像的不同灰度等级数下进行配准,分析了互信息的计算量与灰度等级数的关系,并用人头部的MRI图像和CT图像做了二维的单模模拟实验和多模实际配准实验,结果显示在灰度等级数为32和64时,与灰度等级数为256时相比,配准精度没有明显改变,而计算量下降显著。-Abstract Registration based on mutual information is a typical method in medical image registration. Mutual information is a common similarity measure in image registration, which has excellent robustness and accuracy , but large calculation amount makes it difficult to be applied to clinics. In this paper , a registration algorithm based on multi-resolution and hybrid optimization is adopted to implement 2-dimension monomodal and multimodal registrations of MRI and CT images of human heads with different numbers of gray bins. Results of experiments show that registration precisions have not notable change with 32 , 64 gray bins , compared with 256 gray bins , whereas the computation costs decrease remarkably. Platform: |
Size: 420864 |
Author:vivi |
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Search - Medical Images registration