Mahotas – 去除带边框的标签

在本文中，我们将了解如何从 mahotas 中的标记图像中删除带边框的标签。边框标签是那些与边框接触的标签，我们可以借助 mahotas.label 方法从普通图像创建标记图像。
为此，我们将使用来自核分割基准的荧光显微镜图像。我们可以在下面给出的命令的帮助下获取图像

mhotas.demos.nuclear_image()

下面是nuclear_image

标记图像是整数图像，其中值对应于不同区域。即，区域 1 是所有值为 1 的像素，区域 2 是值为 2 的像素，依此类推
为此，我们将使用 mahotas.remove_bordering 方法

Syntax : mahotas.remove_bordering(labelled)
Argument : It takes labelled image object as argument
Return : It returns the labelled image without label at the borders 
 

示例 1：

Python3

# importing required libraries
import mahotas
import numpy as np
from pylab import imshow, show
import os
 
# loading nuclear image
f = mahotas.demos.load('nuclear')
 
# setting filter to the image
f = f[:, :, 0]
 
# setting gaussian filter
f = mahotas.gaussian_filter(f, 4)
 
# setting threshold value
f = (f> f.mean())
 
# creating a labelled image
labelled, n_nucleus = mahotas.label(f)
 
# showing the labelled image
print("Labelled Image")
imshow(labelled)
show()
 
# removing border labels
labelled = mahotas.labelled.remove_bordering(labelled)
 
# showing the image
print("No border Label")
imshow(labelled)
show()

输出 ：

示例 2：

Python3

# importing required libraries
import numpy as np
import mahotas
from pylab import imshow, show
 
# loading image
img = mahotas.imread('dog_image.png')
   
# filtering the image
img = img[:, :, 0]
    
# setting gaussian filter
gaussian = mahotas.gaussian_filter(img, 15)
 
# setting threshold value
gaussian = (gaussian > gaussian.mean())
 
# creating a labelled image
labelled, n_nucleus = mahotas.label(gaussian)
  
print("Labelled Image")
# showing the gaussian filter
imshow(labelled)
show()
 
# removing border labels
labelled = mahotas.labelled.remove_bordering(labelled)
 
# showing the image
print("No border Label")
imshow(labelled)
show()

输出 ：