RandomWalker分割算法产生与初始种子相同的分割

如何解决RandomWalker分割算法产生与初始种子相同的分割

• 我有一张医学图像，我正在尝试在内部分割特定区域。
• 经过几步常规的图像处理，我能够找到该区域，并设法获得了用于分割的种子，但是当我尝试应用RandomWalker算法时，却没有得到很好的分割。
• 您能告诉我这里是什么问题，以及如何解决吗？

代码：

# import math
import numpy as np
import matplotlib.pyplot as plt
import cv2 as cv
from skimage.feature import canny
from skimage.transform import hough_circle,hough_circle_peaks
from skimage.draw import circle_perimeter
from skimage.segmentation import watershed,random_walker,active_contour
import skimage.filters as filters

# Read image
img = cv.imread("CT.png")

# Get image center coordinates
img_center = (img.shape[0]//2,img.shape[1]//2)

# Edge detector
edges = canny(img,sigma=2.0,low_threshold=19,high_threshold=57)

# Hough_circle
hough_radii = np.arange(29,32,1)
hough_res = hough_circle(edges,hough_radii)
accums,cx,cy,radii = hough_circle_peaks(hough_res,hough_radii,total_num_peaks=4,min_xdistance=70,min_ydistance=200,threshold=0.25)

# Remove false-posite circle
sortX = np.argsort(cx)
cx = cx[sortX[:-1]]
cy = cy[sortX[:-1]]
radii = radii[sortX[:-1]]

#--------------------------------------
# get the closest circle to the centre 
#--------------------------------------
dist = []
for idx in range(len(cx)):
    dist.append(abs(img_center[1]-cx[idx])+abs(img_center[0]-cy[idx]))
sortD = np.argsort(dist)
Cx = cx[sortD[0]]
Cy = cy[sortD[0]]
radius = radii[sortD[0]]

markers = np.ones(img.shape,dtype=np.uint)
markers[img==0] = 0
markers[Cy-radius//2:Cy+radius//2,Cx-radius//2:Cx+radius//2] = 2
# markers[(Cy-radius//2)+1:(Cy+radius//2)-1,(Cx-radius//2)+1:(Cx+radius//2)-1] = 0
#---------------------------------
labels = random_walker(img,markers)

# print(labels.shape)
# Plot results
fig,(ax1,ax2,ax3) = plt.subplots(1,3,figsize=(8,3.2),sharex=True,sharey=True)
ax1.imshow(img,cmap='gray')
ax1.axis('off')
ax1.set_title('Noisy data')
ax2.imshow(markers,cmap='magma')
ax2.axis('off')
ax2.set_title('Markers')
ax3.imshow(labels,cmap='gray')
ax3.axis('off')
ax3.set_title('Segmentation')

fig.tight_layout()
plt.show()
#======================================

解决方法

仅随机漫步者将标记中的标签扩展到标记为0的区域。最后，您得到的图像中除了原始正方形中的2以外，所有地方都只包含一个。这是因为标签2无处可扩：被1包围。

我可以使用以下方法对细分进行一些修改：

border = 71
surround = (
    (dilation(markers,np.ones((border,border))) == 2)
    ^ (markers==2)
)
markers[surround] = 0
labels = random_walker(img,markers) * (img != 0)

它仍然绝对不完美。除此之外，您还需要使用边框大小以及beta=的{​​{1}}和tol=参数。

,

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    "first_name": "John","company": "","address_1": "969 Market","address_2": "","city": "San Francisco","state": "CA","postcode": "94103","country": "US","email": "[e-mail]","phone": "[phone]"
  },"shipping": {
    "first_name": "John","country": "US"
  }
}