如何自动推断处理后的图像是否需要形态学post-处理？

Question

我已经使用深度学习进行了分割，之后，我注意到有时分割的形状似乎很好，有时，它需要更多 post-形态学处理操作.
分割的形状是一个环，在极少数情况下是实心圆，如附图所示。
当这个环分割为闭合形状时，没有问题，但是当环打开时出现问题，因为我无法正确测量内部面积（环内），面积计算是整个项目的objective。
那么如何自动检测我的分割图像是否需要 post 处理（使用形态学操作）以及在这种情况下首选哪种形态学？所以我可以计算所提到的面积 .

提前致谢

import numpy as np
import matplotlib.pyplot as plt
from skimage.io import imread, imsave
# import scipy.ndimage as ndi 
from skimage import morphology, filters, feature

seg = imread('prediction.png')
# meijering alpha=None,
# rem2 = morphology.remove_small_objects(seg, 4)
resf = filters.meijering(seg, sigmas=range(1, 3, 1),  black_ridges=False)

sobel = filters.sobel(resf)
# diam = morphology.diameter_closing(sobel, 64, connectivity=2)
gaussian = filters.gaussian(sobel, sigma= 1)
val = filters.threshold_otsu(gaussian)
resth = gaussian < val 

# Morphology
SE = morphology.diamond(2)
# SE = np.ones((3,3))
# SE = morphology.disk(2)
# SE = square(7)
# SE = rectangle(3,3)
# SE = octagon(3, 3)

erosion  = morphology.binary_erosion( resth, SE).astype(np.uint8)
dilation = morphology.binary_dilation(resth, SE).astype(np.uint8)
opening  = morphology.binary_opening( resth, SE).astype(np.uint8)
closing  = morphology.binary_closing( resth, SE).astype(np.uint8)
#thinner = morphology.thin(erosion, max_iter=4)

rem  = morphology.remove_small_holes(resth, 2)

# entropy  = filters.rank.entropy(resth, SE) 
# print(seg.shape)

plt.figure(num='PProc')
# 1
plt.subplot('335')
plt.imshow(rem,cmap='gray')
plt.title('rem')
plt.axis('off')
# 2
plt.subplot('336')
plt.imshow(dilation,cmap='gray')
plt.title('dilation')
plt.axis('off')
# 3
plt.subplot('337')
plt.imshow(opening,cmap='gray')
plt.title('opening')
plt.axis('off')
# 4
plt.subplot('338')
plt.imshow(closing,cmap='gray')
plt.title('closing')
plt.axis('off')
# 5
plt.subplot('332')
plt.imshow(seg,cmap='gray')
plt.title('segmented')
plt.axis('off')
# 6
plt.subplot('333')
plt.imshow(resf,cmap='gray')
plt.title('meijering')
plt.axis('off')
# 7
# 8
plt.subplot('334')
plt.imshow(resth,cmap='gray')
plt.title('threshold_otsu')
plt.axis('off')
# 9
plt.subplot('339')
plt.imshow(erosion,cmap='gray')
plt.title('erosion')
plt.axis('off')
#
plt.show()

Answer 1

也许这会以某种方式帮助解决问题。图片：

1. Connected Components:4 , Convexy: yes, Euler number(by one object):1
2. Connected Components:1 , Convexy: no, Euler number: 1
3. Connected Components:1 , Convexy: yes, Euler number: 0
4. Connected Components:1 , Convexy: yes, Euler number: 1

欧拉数这是该区域中的对象数减去这些对象中的孔数。参见 skimage.measure.regionprops、https://www.mathworks.com/help/images/ref/regionprops.html 或开源 https://octave.sourceforge.io/image/function/regionprops.html

Answer 2

通过在这个上重现答案并稍微调整一下，答案是：通过检查标记区域的数量，所以如果有少于3个区域，多半需要形态学操作，否则我有一个闭环，然后有3个标记区域。

from skimage.io import imread
import numpy as np
from skimage.measure import label, regionprops
from skimage.color import label2rgb

img = imread(os.path.join(path,im_name))
labeled = label(img, background=2)
overlay = label2rgb(labeled)

LV = overlay[...,0] + overlay[...,1]
LV = LV > 1

if (np.sum(LV)):
    print('This image doesnt need Morphological processing')
else:
    print('This image needs Morphological processing')

如何自动推断处理后的图像是否需要形态学post-处理？

How to deduce automatically if the processed image needs morphological post-processing or not?

python

image-processing

image-segmentation

image-morphology