python - 如何使用分水岭去除脑MRI中的头骨

Question

我正在使用 python 检测脑肿瘤。我的代码中的分段过程需要一些帮助。我想在大脑 MRI 中去除头骨部分。我正在使用分水岭算法来定义 MRI 图像中的头骨。然而，肿瘤区域也被代码遮蔽。如何更改代码检测到的阈值，以保持整个大脑区域不受算法影响。下面显示的图片是我想要得到的样本。` 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()
img           = cv2.imread('s1.png')
gray          = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ShowImage('Brain MRI',gray,'gray')

ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU)
ShowImage('Thresholding image',thresh,'gray')

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)

img = cv2.imread('s1.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)


# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)

# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)

# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)

# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)

# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
 
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1

 
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]

im1 = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
ShowImage('Watershed segmented image',im1,'gray')

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')

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)`

下面显示的图片是我想要得到的样本。这是我运行代码时得到的这是原始图像

标签： pythonimage-processingimage-segmentationscikit-imagewatershed