如何从二进制图像中分离单元格

我们也可以这样做 scikit-image morphology's label() 通过在二值图像中查找连接的组件来实现。但找到的单元格数为421。 Morphologial 操作,如 erosion / dilation / closing / opening 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
import matplotlib.pyplot as plt

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

以下是已识别和分离的细胞:

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。