MRI（脑肿瘤）图像处理和分割，颅骨去除
MRI (brain tumor) image processing and segmentation, skull removing
我需要图像分割方面的帮助。我有脑部肿瘤的 MRI 图像。我需要从 MRI 中移除颅骨（头骨），然后仅分割肿瘤对象。我怎么能在 python 中做到这一点？与图像处理。我试过制作轮廓，但我不知道如何找到并移除最大的轮廓，只得到没有头骨的大脑。
非常感谢。
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：
感谢您的回答。
预赛
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，这是您链接到的大脑+头骨之一：
#Read in image
img = cv2.imread('brain.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ShowImage('Brain with Skull',gray,'gray')
提取掩码
如果图像中的像素可以分为两种不同的强度类，也就是说，如果它们具有双峰直方图，那么Otsu's method可以用来将它们阈值化为二值掩码.让我们检查一下这个假设。
#Make a histogram of the intensities in the grayscale image
plt.hist(gray.ravel(),256)
plt.show()
好的，数据是双峰的。让我们应用阈值，看看我们是怎么做的。
#Threshold the image to binary using Otsu's method
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU)
ShowImage('Applying Otsu',thresh,'gray')
如果我们将蒙版叠加到原始图像上，事情会更容易看清
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')
提取大脑
大脑（以红色显示）与面具的重叠非常完美，我们就在这里停下来。为此，让我们提取连接的组件并找到最大的组件，这将是大脑。
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')
考虑第二大脑
运行 你的第二张图片再次生成一个有很多洞的面具：
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')
我们现在可以提取大脑了：
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')
如果您出于某种原因需要引用此内容：
Richard Barnes. (2018). Using Otsu's method for skull-brain segmentation (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.6042312
您是否尝试过使用 python skull_stripping.py
您可以修改参数，但通常效果很好。
有一些使用深度学习进行颅骨剥离的新研究，我觉得很有趣：
https://github.com/mateuszbuda/brain-segmentation/tree/master/skull-stripping
# -*- 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)
-----------------------------------------
Input Image
--------------------
我需要图像分割方面的帮助。我有脑部肿瘤的 MRI 图像。我需要从 MRI 中移除颅骨（头骨），然后仅分割肿瘤对象。我怎么能在 python 中做到这一点？与图像处理。我试过制作轮廓，但我不知道如何找到并移除最大的轮廓，只得到没有头骨的大脑。 非常感谢。
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：
感谢您的回答。
预赛
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，这是您链接到的大脑+头骨之一：
#Read in image
img = cv2.imread('brain.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ShowImage('Brain with Skull',gray,'gray')
提取掩码
如果图像中的像素可以分为两种不同的强度类，也就是说，如果它们具有双峰直方图，那么Otsu's method可以用来将它们阈值化为二值掩码.让我们检查一下这个假设。
#Make a histogram of the intensities in the grayscale image
plt.hist(gray.ravel(),256)
plt.show()
好的，数据是双峰的。让我们应用阈值，看看我们是怎么做的。
#Threshold the image to binary using Otsu's method
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU)
ShowImage('Applying Otsu',thresh,'gray')
如果我们将蒙版叠加到原始图像上，事情会更容易看清
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')
提取大脑
大脑（以红色显示）与面具的重叠非常完美，我们就在这里停下来。为此，让我们提取连接的组件并找到最大的组件，这将是大脑。
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')
考虑第二大脑
运行 你的第二张图片再次生成一个有很多洞的面具：
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')
我们现在可以提取大脑了：
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')
如果您出于某种原因需要引用此内容：
Richard Barnes. (2018). Using Otsu's method for skull-brain segmentation (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.6042312
您是否尝试过使用 python skull_stripping.py 您可以修改参数，但通常效果很好。
有一些使用深度学习进行颅骨剥离的新研究，我觉得很有趣：
https://github.com/mateuszbuda/brain-segmentation/tree/master/skull-stripping
# -*- 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)
-----------------------------------------
Input Image
--------------------