识别大脑图像是正常图像还是肿瘤图像 [英] recognizing the brain image is normal image or tumor image

问题描述

谁能告诉我识别输入图像是正常的大脑图像还是病变(肿瘤)图像的代码.我们已经成功地使用k均值聚类对大脑图像进行了分割.

can anyone tell me the code for recognizing whether the input image is normal brain image or lesion(tumor) image.we have successfully segmented the brain image using k means clustering.

推荐答案

我确定某人某个地方拥有他们已被整个团队使用数月或数年的代码，准备好了，不， 发痒 将其传递给您，以便您既可以将其声明为您自己的家庭作业，也可以在与他竞争的公司中成立公司.

但是他们不住在这里.

抱歉.

I''m sure that someone, somewhere, has the code that they have slaved over with their entire team for months or years, just ready, nay, itching to pass it on to you, so that you can either claim it as your own homework or set up a company in competition to his.

But they don''t live here.

Sorry.

根据您所说的话，我推断这是一个玩具项目.

如果您的细分工作可靠，则应该有较大且稳定的区域.确保仅保留这些区域并丢弃混乱.

肿瘤应区分为意想不到的灰度级区域，形状与正常区域不同.

假设您具有正常大脑的参考图像，并且不需要进行配准(所有图像的头部都在完全相同的位置拍摄)，则可以将区域两两比较，例如使用Jaccard相似度测量( http://en.wikipedia.org/wiki/Jaccard_coefficient [

From the little that you say, I infer that this is a toy project.

If your segmentation works reliably, you should have large, stable regions. Make sure that you keep only such regions and discard clutter.

A tumor should be distinguished as a region of an unexpected gray level, differing in shape from a normal region.

Assuming that you have a reference image of a normal brain, and also assuming that no registration is required (all images being taken with the heads in exactly the same position), you can compare the regions two by two, for instance using the Jaccard similarity measure (http://en.wikipedia.org/wiki/Jaccard_coefficient[^]). It is an easy matter to count all pixels in the interesections and unions (though the pairwise comparison process will be very costly).

Abnormal regions will appear as having a low Jaccard value.

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