MRI (脑肿瘤)图像处理和分割，颅骨切除

Question

我需要图像分割的帮助。我有脑部肿瘤的核磁共振图像。我需要从MRI中移除头盖骨，然后只分割肿瘤对象。我如何在python中做到这一点呢？具有图像处理功能。我试过画轮廓线，但我不知道如何找到并移除最大的轮廓线，只得到没有头骨的大脑。太感谢了。

def get_brain(img):
  row_size = img.shape[0]
  col_size = img.shape[1]

  mean = np.mean(img)
  std = np.std(img)
  img = img - mean
  img = img / std

 middle = img[int(col_size / 5):int(col_size / 5 * 4), int(row_size / 5):int(row_size / 5 * 4)]
  mean = np.mean(middle)
  max = np.max(img)
  min = np.min(img)


  img[img == max] = mean
  img[img == min] = mean

  kmeans = KMeans(n_clusters=2).fit(np.reshape(middle, [np.prod(middle.shape), 1]))
  centers = sorted(kmeans.cluster_centers_.flatten())
  threshold = np.mean(centers)
  thresh_img = np.where(img < threshold, 1.0, 0.0)  # threshold the image


  eroded = morphology.erosion(thresh_img, np.ones([3, 3]))
  dilation = morphology.dilation(eroded, np.ones([5, 5]))

这些图像与我正在查看的图像相似：

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谢谢你的回答。

Answer 1

前言

一些初步代码：

%matplotlib inline
import numpy as np
import cv2
from matplotlib import pyplot as plt
from skimage.morphology import extrema
from skimage.morphology import watershed as skwater

def ShowImage(title,img,ctype):
  plt.figure(figsize=(10, 10))
  if ctype=='bgr':
    b,g,r = cv2.split(img)       # get b,g,r
    rgb_img = cv2.merge([r,g,b])     # switch it to rgb
    plt.imshow(rgb_img)
  elif ctype=='hsv':
    rgb = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
    plt.imshow(rgb)
  elif ctype=='gray':
    plt.imshow(img,cmap='gray')
  elif ctype=='rgb':
    plt.imshow(img)
  else:
    raise Exception("Unknown colour type")
  plt.axis('off')
  plt.title(title)
  plt.show()

作为参考，这里有一个您链接到的brain+skulls：

#Read in image
img           = cv2.imread('brain.png')
gray          = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ShowImage('Brain with Skull',gray,'gray')

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提取掩码

如果图像中的像素可以分为两个不同的强度类别，也就是说，如果它们具有双峰直方图，则可以使用Otsu's method将它们阈值化为二进制掩模。让我们检验一下这个假设。

#Make a histogram of the intensities in the grayscale image
plt.hist(gray.ravel(),256)
plt.show()

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好的，数据是双峰的。让我们应用阈值，看看我们是如何做到的。

#Threshold the image to binary using Otsu's method
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU)
ShowImage('Applying Otsu',thresh,'gray')

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如果我们将蒙版叠加到原始图像上，事情就更容易看出来了

colormask = np.zeros(img.shape, dtype=np.uint8)
colormask[thresh!=0] = np.array((0,0,255))
blended = cv2.addWeighted(img,0.7,colormask,0.1,0)
ShowImage('Blended', blended, 'bgr')

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提取大脑

大脑(以红色显示)与面具的重叠是如此完美，我们就到此为止。为此，让我们提取连接的组件并找到最大的组件，即大脑。

ret, markers = cv2.connectedComponents(thresh)

#Get the area taken by each component. Ignore label 0 since this is the background.
marker_area = [np.sum(markers==m) for m in range(np.max(markers)) if m!=0] 
#Get label of largest component by area
largest_component = np.argmax(marker_area)+1 #Add 1 since we dropped zero above                        
#Get pixels which correspond to the brain
brain_mask = markers==largest_component

brain_out = img.copy()
#In a copy of the original image, clear those pixels that don't correspond to the brain
brain_out[brain_mask==False] = (0,0,0)
ShowImage('Connected Components',brain_out,'rgb')

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考虑到第二个大脑

对第二张图像再次运行此操作，会生成一个带有许多孔的蒙版：

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我们可以使用closing transformation来关闭其中的许多漏洞

brain_mask = np.uint8(brain_mask)
kernel = np.ones((8,8),np.uint8)
closing = cv2.morphologyEx(brain_mask, cv2.MORPH_CLOSE, kernel)
ShowImage('Closing', closing, 'gray')

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我们现在可以提取大脑：

brain_out = img.copy()
#In a copy of the original image, clear those pixels that don't correspond to the brain
brain_out[closing==False] = (0,0,0)
ShowImage('Connected Components',brain_out,'rgb')

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(请注意，如果您将此用于学术目的，则学术诚信需要适当的归因。详情请联系我。)

Answer 2

您可能尝试过使用python skull_stripping.py，您可以修改参数，但它通常运行良好。

有一些新的研究使用深度学习来剥离头骨，我觉得很有趣：

https://github.com/mateuszbuda/brain-segmentation/tree/master/skull-stripping

Answer 3

# -*- coding: utf-8 -*-
"""
Created on Wed Jul 28 17:10:56 2021

@author: K Somasundaram, ka.somasundaram@gmail.com
"""
import numpy as npy
from skimage.filters  import threshold_otsu
from skimage import measure
# import image reading module image from matplotlib
import matplotlib.image as img
#import image ploting module pyplot from matplotlib 
import matplotlib.pyplot as plt
inim=img.imread('015.bmp')
#Find the dimension of the input image
dimn=inim.shape
print('dim=',dimn)
plt.figure(1)
plt.imshow(inim)
#-----------------------------------------------
# Find a threshold for the image using Otsu method in filters
th=threshold_otsu(inim)
print('Threshold  = ',th)
# Binarize using threshold th
binim1=inim>th
plt.figure(2)
plt.imshow(binim1)
#--------------------------------------------------
# Erode the binary image with a structuring element
from skimage.morphology import disk
import skimage.morphology as morph
#Erode it with a radius of 5
eroded_image=morph.erosion(binim1,disk(3))
plt.figure(3)
plt.imshow(eroded_image)
#---------------------------------------------
#------------------------------------------------

# label the binar image
labelimg=measure.label(eroded_image,background=0)
plt.figure(4)
plt.imshow(labelimg)

#--------------------------------------------------
# Find area of the connected regiond
prop=measure.regionprops(labelimg)
# Find the number of objecte in the image 
ncount=len(prop)
print ( 'Number of regions=',ncount)
#-----------------------------------------------------
# Find the LLC index
argmax=0
maxarea=0
#Find the largets connected region
for i in range(ncount):
    if(prop[i].area >maxarea):
      maxarea=prop[i].area
      argmax=i
print('max area=',maxarea,'arg max=',argmax)

print('values=',[region.area for region in prop])
# Take only the largest connected region
 # Generate a mask of size of th einput image with all zeros
bmask=npy.zeros(inim.shape,dtype=npy.uint8)
# Set all pixel values in whole image to the LCC index to 1
bmask[labelimg == (argmax+1)] =1


plt.figure(5)
plt.imshow(bmask)

#------------------------------------------------
#Dilate the isolated region to recover the pixels lost in erosion
dilated_mask=morph.dilation(bmask,disk(6))
plt.figure(6)
plt.imshow(dilated_mask)
#---------------------------------------
# Extract the brain using the barinmask
brain=inim*dilated_mask
plt.figure(7)
plt.imshow(brain)

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Input Image
--------------------

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