去除显微图像中线状取向的沉淀物

Question

我有一些显微图像，其中存在单一状态的沉淀物，并且在一些图像中存在水平或垂直线的沉淀物。现在我应该如何删除这些行？

import matplotlib.pyplot as plt
import numpy as np
from scipy import ndimage as ndi
import cv2
import math
from skimage import (
color, feature, filters, measure, morphology, segmentation, util
)



# Sample1 - T61
image = cv2.imread(r"C:\Users\Stelle1.tif",cv2.IMREAD_GRAYSCALE)
assert not isinstance(image,type(None)), 'image not found'

fig, ax = plt.subplots()
ax.imshow(image, cmap='gray')
ax.axis('off')
plt.imshow()

点击查看图片

fig, ax = plt.subplots(figsize=(5, 5))
qcs = ax.contour(image, origin='image')
ax.axis('off')
plt.show()

thresholds = filters.threshold_multiotsu(image, classes=3)
regions = np.digitize(image, bins=thresholds)

fig, ax = plt.subplots(ncols=2, figsize=(10, 5))
ax[0].imshow(image)
ax[0].set_title('Original')
ax[0].axis('off')
ax[1].imshow(regions)
ax[1].set_title('Multi-Otsu thresholding')
ax[1].axis('off')
plt.show()


cells = image > thresholds[0]
dividing = image > thresholds[1]
labeled_cells = measure.label(cells)
labeled_dividing = measure.label(dividing)
naive_mi = labeled_dividing.max() / labeled_cells.max()
print(naive_mi)

higher_threshold = 100
dividing = image > higher_threshold

smoother_dividing = filters.rank.mean(util.img_as_ubyte(dividing),
                                  morphology.disk(4))

binary_smoother_dividing = smoother_dividing > 20

fig, ax = plt.subplots(figsize=(5, 5))
ax.imshow(binary_smoother_dividing)
ax.set_title('Dividing precipitate')
ax.axis('off')
plt.show()

点击查看图像

这是我得到的结果，如果我增加 higher_threshold = 100，我将失去椭圆形状沉淀，我需要计算面积和其他属性。您能否提出一些解决方案，使算法不应检测线形沉淀？

Answer 1

您是否考虑过使用霍夫变换之类的东西来检测直线？：
https://scikit-image.org/docs/dev/auto_examples/ Edges/plot_line_hough_transform.html

我基本上直接从上面的教程中提取了这个，并得到了一些相当不错的开箱即用的结果。

from skimage import io
from skimage.transform import probabilistic_hough_line
from skimage.feature import canny


img = io.imread('GsSj9.png', as_gray=True) # read in the image 
edges = canny(img) # use canny filter to detect edges
lines = probabilistic_hough_line(edges, threshold=20, line_length=20, line_gap=3)

# make plot of image and probabilistic_hough_line
fig, axes = plt.subplots(1, 2, sharex=True, sharey=True)
ax = axes.ravel()

ax[0].imshow(img)
ax[0].set_title('image')
ax[1].imshow(img * 0)
for line in lines:
    p0, p1 = line
    ax[1].plot((p0[0], p1[0]), (p0[1], p1[1]))
ax[1].set_xlim((0, img.shape[1]))
ax[1].set_ylim((img.shape[0], 0))
ax[1].set_title('Probabilistic Hough')

您仍然需要找出一种从变换线制作二值图像的好方法，但它可能对您的努力有用。