图像二值化#

在对图像进行二值化时，我们生成的图像只有两个值：真和假。它们也可能包含值0，例如用于背景，而其他任何值用于前景。

import numpy as np
from skimage.io import imread, imshow
from skimage.filters import gaussian

我们使用这个细胞核的示例图像。

image_nuclei = imread('../../data/mitosis_mod.tif')

imshow(image_nuclei)

<matplotlib.image.AxesImage at 0x1dcf6b5dbe0>
../_images/7965ce710e547039cf2bfd6f886f9e6d38b6871ba74d6654aa2ead048f7a6139.png

图像阈值化#

最常见的二值化技术是阈值化。我们_应用阈值_来确定哪些像素的强度高于某个特定值，哪些低于该值。

image_binary = image_nuclei > 60

imshow(image_binary)

<matplotlib.image.AxesImage at 0x1dcf6bb09a0>
../_images/f9dc81389fae7ada02ab802bc2913226a503458279155021e90d22da542c4583.png

改善二值化结果#

有时，二值化结果可能看起来像素化。通过在阈值化图像之前应用滤波器可以改善这一点。

image_denoised = gaussian(image_nuclei, sigma=1, preserve_range=True)
imshow(image_denoised, cmap='Greys_r')

C:\Users\haase\mambaforge\envs\bio39\lib\site-packages\skimage\io\_plugins\matplotlib_plugin.py:150: UserWarning: Float image out of standard range; displaying image with stretched contrast.
  lo, hi, cmap = _get_display_range(image)

<matplotlib.image.AxesImage at 0x1dcf6c48640>
../_images/006dab8b724854f8cf48fec1444bdf4ff05d0157178c879b89cbf70b2a450296.png 
image_binary2 = image_denoised > 60

imshow(image_binary2)

<matplotlib.image.AxesImage at 0x1dcf7d61040>
../_images/623c89c9fd796957b3265b5695f0832a70ea3680d5fd8a0aed5429af5ab72d12.png