是否可以比较3D图像？ [英] Is it possible to compare 3D images?

问题描述

是否可以比较两个3D图像？
如果可能的话，可能的解决方案是什么（我的意思是方法）？

Is it possible to compare two 3D images? If possible what are the possible solutions(i mean methods)?

目的我要比较两个3D图像来识别该源中的对象图片。
即使我还有一个问题，目前我一直在比较将3d图像转换成2d，但我几乎不习惯通过相关来匹配它们。

And the purpose i want to compare two 3D images it to identify an object in that source image. even i have one more question, at present i have been comparing converting 3d images into 2d, but i was barely used to match them by correlating.

如何我可以检测到图像中的对象（ROI）？
如果检测到如何形成对象的边框线？
除了离开对象之外，如何删除图像中的其他数据？

How i can detect the object in an image(ROI)? How to form border lines of the object if detected? How to remove the other data in the image except leaving the object?

推荐答案

您刚刚询问了所有可能的问题在3D图像处理中。你不会得到一个简洁的答案。请分开，然后征服。请在下面找到几个关键字以便正常运行！

You just asked about all possible questions in 3D image processing. You will not get a neat answer. Please divide, and then conquer. Please find below a few keyword to put on on track!

是否可以比较两个3D图像？
- >图像相似性指标，如均方，标准化关联，模式强度，互信息
- >只有在图像注册时才能执行此操作。检查严格注册或非刚性注册。

Is it possible to compare two 3D images? -> image similarity metrics, such as Mean Squares, Normalized Correlation, Pattern Intensity, Mutual Information -> you can only do it if your images are registered. Check rigid registration or non-rigid registration.

识别该源图像中的对象。
- >这是分段（3D）或计算机视觉（2D）的主题

Identify an object in that source image. -> that is the topic of segmentation (in 3D), or computer vision (in 2D)

如何检测图像中的对象（ROI） ）？
如果检测到如何形成对象的边框线？
- >这是边缘检测的主题，例如参见Canny Edge。

How i can detect the object in an image(ROI)? How to form border lines of the object if detected? -> that is the topic of Edge detection, see Canny Edge for example.

除了离开对象外，如何删除图像中的其他数据？
- >琐碎，你只需减去它就可以减去。

How to remove the other data in the image except leaving the object? -> trivial, you just subtract once you've it.

医学影像中的大量资源。了解ITK可以做些什么。一些开源软件包括3DSlicer，3DSeg，......

Lots of resources in Medical Imaging. See what ITK can do. Some open source software include 3DSlicer, 3DSeg, ...

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