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医学图像分割

[英]Medical Image Segmentation

I'm looking for a segmentation algorithm to extract the middle part of the mouse spine from MRI image shown below. 我正在寻找一种分割算法,用于从下面显示的MRI图像中提取鼠标脊柱的中间部分。 I thought about extracting the part by thresholding intensity but the intensity is too similar to other parts in the image. 我曾考虑过通过阈值强度来提取部分,但是强度与图像中的其他部分过于相似。 I was also told (by my supervisor) to look into semi-automated segmentation algorithms where you would click on a pixel in the region of interest and the algorithm would consider pixels spreading from the one you clicked. 我的主管还告诉我,应研究半自动分割算法,在该算法中,您可以单击感兴趣区域中的一个像素,而该算法将考虑从所单击的像素开始扩散的像素。 Any additional advice on this would also be appreciated. 任何对此的任何其他建议也将不胜感激。

I'm new to this field so a simple explanation and/or links to additional resources are welcome. 我是该领域的新手,欢迎提供简单的说明和/或指向其他资源的链接。

MRI image of mouse spine: 小鼠脊柱的MRI图像:

IMG

Same as above with highlighted region that needs to be segmented: 与上述相同,但突出显示的区域需要细分:

IMG

PS - I'm working in matlab so if you know of a good toolbox, please let me know. PS-我在matlab工作,所以如果您知道一个好的工具箱,请告诉我。

My background: Undergrad in Physics, starting Medical Physics MSc, and trying to get into image analysis / computer vision. 我的背景:物理本科生,创建医学物理理学硕士,并尝试进入图像分析/计算机视觉。

Your image has very poor resolution, it's very difficult to extract desired regions. 您的图像分辨率很差,提取所需区域非常困难。 Try in ImageJ, it's much easier to select region of interest (ROI) and to calculate area and other parameters. 在ImageJ中尝试时,选择感兴趣区域(ROI)并计算面积和其他参数要容易得多。

Edit: 编辑:

If you want to stick with MATLAB, you can make use of roipoly() . 如果您想坚持使用MATLAB,可以使用roipoly() It's not either automated or semi automated algo. 它既不是自动化算法也不是半自动化算法。 Basically what you will be doing is, clicking multiple points on the boundary of your desired region and then measure the number of pixels within the boundary. 基本上,您要做的是单击所需区域边界上的多个点,然后测量边界内的像素数。 Following code does the same: 以下代码执行相同的操作:

img = imread('your_image');
BW = roipoly(img); %Select points (complete the loop)-> right click -> create mask
close;
number_of_pixles = length(find(BW == 1)) %Total number of pixels in the region.

I am dealing with the same type of MRI data, low resolution and tiny structure with the same intensity distribution to other struction. 我正在处理相同类型的MRI数据,低分辨率和结构细小且强度相同的其他结构。 What I did to make it easier, is to build a template for the unrelated structures and use them to cut them away from my original image so that I can have the VOI I need, Then I used Random Forest (or you can start with region growing and define some regularization and smoothing parameter) to extract your region. 我要做的是,为不相关的结构构建一个模板,并使用它们将其与原始图像切开,以便获得所需的VOI,然后我使用了Random Forest(或者您可以从区域开始并定义一些正则化和平滑参数)以提取您的区域。

You can start some tests on ITKsnap the package segmentation. 您可以在ITKsnap程序包分段上开始一些测试。 there is a semi automatic supervised learning algorithm based on level sets active contours for segmentation. 有一种基于水平集活动轮廓线的半自动监督学习算法进行分割。 I used it and I found it very helpful. 我使用了它,发现它非常有用。 Please let me know if you need help for it. 如果您需要帮助,请告诉我。

You might consider looking at the spatial/spectral characteristics of the spinal region in a sample of images. 您可以考虑在图像样本中查看脊柱区域的空间/光谱特征。 That way you can leverage some characteristics of the structure to help you segment the ROI. 这样,您可以利用结构的某些特征来帮助您划分投资回报率。

The idea is to make it more about the micro morphology than about the intensity level which might vary with different samples. 想法是使它更多地关于微观形态,而不是关于强度水平,强度水平可能随不同样品而变化。

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