从MRI图像中去除伪像 [英] to remove artifacts from mri image

问题描述

我想要一个C#代码来删除工件，例如患者的姓名，年龄，D.O.B
使用跟踪算法

可以从以下网址引用该算法: http://paper.ijcsns.org/07_book/200911/20091138.pdf [ ^ ]

我们正在工作的图像为: http://1.bp.blogspot .com/_YD9VVVy0saU/StfM4C5PmbI/AAAAAAAAAMM/5rRjvOPXNN0/s400/oli.jpg [

I want a c# code for removing artifacts such as patient''s name, age,D.O.B
using traking algorithm

algorithm can be referred from: http://paper.ijcsns.org/07_book/200911/20091138.pdf[^]

the image on whic we are working is :http://1.bp.blogspot.com/_YD9VVVy0saU/StfM4C5PmbI/AAAAAAAAAMM/5rRjvOPXNN0/s400/oli.jpg[^]

this is first module of our project "brain tumor detection" and we have submission of this module very soon.please help.

推荐答案

首先，我必须说我感到震惊:我们再次面对人类集体愚昧的例子，这是技术中嵌入的社会结构的瘫痪.

在MDI嵌入式计算机的内部某处，MDI图像识别算法建立了人体一部分(例如头部)的模型.从数学上讲，MDI算法是我们可以在工业生产的任何技术产品中找到的最复杂的程序之一，可能是全人类工业所生产的产品中最复杂的程序.在输出上，成像数据与嵌入的文本数据一起输出，该嵌入的文本数据是使用人员手动输入的患者数据生成的.显然，此类数据在嵌入此文本数据之前就已存在，仅通过读取就不能以合法"的方式对其进行访问.至少对于参与技术链的某些人而言.

它看起来不像是单个公司使用的骇客.根据文章，此黑客是由作者完成的.让我们来看看.

阅读这篇文章给人留下深刻的文盲印象，以及他们看起来科学"的愿望.本文专门讨论降噪，跟踪算法"与此无关.他们将跟踪算法"称为跟踪算法"，用于删除非常嵌入的文本伪像，称为电影伪像".每个曾经听说过MRI和所谓的断层扫描"的人都知道，从来没有像电影"这样的东西.好吧，让我们将其视为专业"行话.

真正的问题是，根据描述，没有任何跟踪算法".丢掉奇怪的术语标志值"，作者似乎简单地用一个像素一个像素地用黑色像素替换了白色像素，希望大脑图像本身仅由灰色"像素组成(而不是值255)，所以与有用"图像中的对比度较低相比，由于黑色背景上的文本具有极高的对比度，因此可以识别"文本伪像.

这种算法"是高度不可靠的.如果背景色与0值(例如1-2)略有不同，则文本将保持清晰可读(并且可以通过简单的对比算法检测).更糟糕的是，将该算法应用于整个图像时，会损坏图像有用部分中的某些像素.

脑部损伤！

我可能仍然适用于大多数或所有情况，只是尝试使用它即可.我希望手动选择要清理的区域，但是在这种情况下，根本不需要算法.有更复杂的算法可以更可靠地完成这项工作，并且使用它们是一门完整的科学.您可以找到它们:
http://en.wikipedia.org/wiki/AForge.NET [ http ://www.aforgenet.com/ [ ^ ]，
http://en.wikipedia.org/wiki/OpenCV [ http://opencv .willowgarage.com/wiki/ [ ^ ].在复杂性方面，与MRI算法相比，所有这些算法都不算什么.

大多数图像识别项目都是商业化的.

—SA

First of all, I must say I''m shocked: once again, we face an example of human collective idiocy, a paralysis of social structures embedded in the technology.

Somewhere, in the internals of the MDI embedded machine, the MDI image recognition algorithm builds a model of a part of human body, such as a head. The MDI algorithm, mathematically, is one of the most sophisticated procedures we can find in any products of technology produced by industry, probably the most sophisticated in the what is produced by the industries of the whole humankind, ever. And on the output, the imaging data comes out with the embedded text data generated using patient data manually entered be the personnel. It is apparent that such data exists before embedding of this text data, it is not made accessible in a "legal" way, just by reading it. At least for some of people involved in a technological chain.

It does not look like a hack used by a single company. According to the article, this hack is done by the authors. Let''s see.

Reading of the article leave impression of deep illiteracy of the authors and their desire to look "scientific". The article is devoted to de-noising, and the "tracking algorithm" has nothing to do with it. They call "tracking algorithm" something they use to remove that very embedded text artifact the call "film artifact". Every one who ever called about MRI and what is called "tomography" knows that there is no such thing as a "film", ever. OK, let''s dismiss it as a "professional" jargon.

The real problem is that, according to the description, there is no any "tracking algorithm". Throwing out strange term "flag value", it looks like the authors simply replace white pixels with black pixels, pixel by pixel, in hope that the brain image itself is only composed with "gray" pixels (not of the value 255), so the text artifacts are "recognized" due to extreme contrast in the text on black background compared to less contrast in the "useful" image.

This "algorithm" is highly unreliable. The text would remain clearly readable (and detectable by simple contrasting algorithm) if background color is slightly different from the value of 0 (say, 1-2). Worst, the algorithm, when applied to whole image, can damage some pixels in the useful part of image.

Brain damage!

I might still be applicable to most or all cases though, just try to use it. I would prefer manual selection of the areas to be cleaned up, but in this case, no algorithm is needed at all. There are more sophisticated algorithms which can do this job more reliably, and using them is a whole science. You can find them:
http://en.wikipedia.org/wiki/AForge.NET[^], http://www.aforgenet.com/[^],
http://en.wikipedia.org/wiki/OpenCV[^], http://opencv.willowgarage.com/wiki/[^]. In complexity, all such algorithms are nothing compared to MRI algorithms.

Most image recognition projects are commercial.

—SA

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