我如何从二进制图像中分离单元格

Question

我有一个细胞的二进制图像。我想使用python单独分离这些单元格。每个单元格将保存在图像中。例如，我有1000个单元格，然后输出将是1000个图像，每个图像包含1个单元格。目前，我使用两种方法来获取它，但输出是错误的

from skimage.morphology import watershed
from skimage.feature import peak_local_max
from skimage import morphology
import numpy as np
import cv2
from scipy import ndimage
from skimage import segmentation

image=cv2.imread('/home/toanhoi/Downloads/nuclei/9261_500_f00020_mask.png',0)
image=image[300:600,600:900]
# First way: peak_local_max
distance = ndimage.distance_transform_edt(image)
local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)), labels=image)
markers = morphology.label(local_maxi)
labels_ws = watershed(-distance, markers, mask=image)
markers[~image] = -1
labels_rw = segmentation.random_walker(image, markers)
cv2.imshow('watershed',labels_rw)
cv2.waitKey(5000)
# Second way way: using contour
_,contours,heirarchy=cv2.findContours(image,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(image,contours,-1,(125,125,0),1)

cv2.imshow('contours',image)
cv2.waitKey(5000)

Answer 1

一种方法是，

import numpy as np
import cv2

img = cv2.imread('sLUel.png')       # image provided in question
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
mask = np.zeros(img.shape[:2],np.uint8)

_,contours,hierarchy = cv2.findContours(img,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)
count = 0

for i, c in enumerate(contours):
    if hierarchy[0,i,3] != -1:
        continue

    mask_copy = mask.copy()

    cv2.drawContours(mask_copy,c,-1,(255,255,255),-1)
    cv2.floodFill(mask_copy,None,(0,0),255)
    mask_inv=cv2.bitwise_not(mask_copy)

    cv2.imwrite(str(count)+'.png', mask_inv)
    count+=1

图像中有420个单元格。

Answer 2

通过查找二进制图像中的连通分量，可以对scikit-image morphology's label()函数执行相同的操作。发现的细胞数量是421。 Morphologial等erosion / dilation / closing / opening操作也可用于预处理输入图像并获得所需的输出。

from skimage import morphology as morph
from skimage.io import imread, imsave
from skimage.color import rgb2gray
import numpy as np

im = rgb2gray(imread('sLUel.png'))
#im = (im > 0).astype(np.uint8)

#labeled = morph.label(morph.binary_opening(im, selem=morph.disk(radius=2)), connectivity=2)
labeled = morph.label(im, connectivity=2)

print(len(np.unique(labeled)))
for i in np.unique(labeled)[1:]: # skip the first component since it's the background
    im_obj = np.zeros(im.shape) 
    im_obj[labeled == i] = 1
    imsave('sLUel_{:03d}.png'.format(i), im_obj)

plt.figure(figsize=(20,10))
plt.subplot(121), plt.imshow(im), plt.axis('off'), plt.title('original binary image', size=15)
plt.subplot(122), plt.imshow(labeled, cmap='spectral'), plt.axis('off'), plt.title('connected components (radius 2)', size=15)
plt.show()

具有以下输出

以下是识别和分离的细胞：