如何改进 Python 中异质结构的分水岭分割？

Question

我在 Python 中使用分水岭算法遵循一种简单的方法来分割细胞（显微图像）。 90% 的时间我对结果都很满意，但我有两个主要问题：(i) markers/contours 真的很“尖锐”，(2) 当两个单元格彼此靠近时，算法有时会失败其他（即它们被分割在一起）。您能提供一些改进建议吗？

这是我正在使用的代码和显示我的 2 个问题的输出图像。

# Adjustable parameters for a future function
img_file = NP_file
sigma = 9 # size of gaussian blur kernel; has to be an even number
alpha = 0.2 #scalling factor distance transform
clear_border = False
remove_small_objects = True

# read image and covert to gray scale 
im = cv2.imread(NP_file, 1)
im = enhanceContrast(im)
im_gray = cv2.cvtColor(im.copy(), cv2.COLOR_BGR2GRAY)

# Basic Median Filter
im_blur = cv2.medianBlur(im_gray, ksize = sigma)

# Threshold Image
th, im_seg = cv2.threshold(im_blur, im_blur.mean(), 255, cv2.THRESH_BINARY);

# filling holes in the segmented image
im_filled = binary_fill_holes(im_seg)

# discard cells touching the border
if clear_border == True: 
    im_filled = skimage.segmentation.clear_border(im_filled)

# filter small particles
if remove_small_objects == True: 
    im_filled = sk.morphology.remove_small_objects(im_filled, min_size = 5000)

# apply distance transform
# labels each pixel of the image with the distance to the nearest obstacle pixel.
# In this case, obstacle pixel is a boundary pixel in a binary image.

dist_transform = cv2.distanceTransform(img_as_ubyte(im_filled), cv2.DIST_L2, 3)

# get sure foreground area: region near to center of object
fg_val, sure_fg = cv2.threshold(dist_transform, alpha * dist_transform.max(), 255, 0)

# get sure background area: region much away from the object
sure_bg = cv2.dilate(img_as_ubyte(im_filled), np.ones((3,3),np.uint8), iterations = 6)
    
# The remaining regions (borders) are those which we don’t know if they are img or background
borders = cv2.subtract(sure_bg, np.uint8(sure_fg))

# use Connected Components labelling: 
# scans an image and groups its pixels into components based on pixel connectivity
# label background of the image with 0 and other objects with integers starting from 1.

n_markers, markers1 = cv2.connectedComponents(np.uint8(sure_fg))

# filter small particles again! (bc of segmentation artifacts)
if remove_small_objects == True: 
    markers1 = sk.morphology.remove_small_objects(markers1, min_size = 1000)
    
# Make sure the background is 1 and not 0; 
# and that borders are marked as 0
markers2 = markers1 + 1
markers2[borders == 255] = 0

# implement the watershed algorithm: connects markers with original image
# The label image will be modified and the marker in the border area will change to -1
im_out = im.copy()
markers3 = cv2.watershed(im_out, markers2)

# generate an extra image with color labels only for visuzalization
# color markers in BLUE (pixels = -1 after watershed algorithm)
im_out[markers3 == -1] = [0, 255, 255]

如果您想重现我的结果，您可以在此处找到我的 .tif 文件： https://drive.google.com/file/d/13KfyUVyHodtEOP_yKAnfFCAhgyoY0BQL/view?usp=sharing

谢谢！

Answer 1

以前，我应用分水岭算法的最佳途径是'only when needed'。它是计算密集型的，图像中的大多数细胞都不需要。这是我对你的图片使用的代码：

# Threshold your image
# This example worked very well with a threshold value of 1
tv, thresh = cv2.threshold(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY), 1, 255, cv2.THRESH_BINARY)

# Minimize the holes in the cells to facilitate finding contours
for i in range(5):
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, np.ones((3,3)))
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, np.ones((3,3)))

# Find contours and keep the ones big enough to be a cell
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = [c for c in contours if cv2.contourArea(c) > 400]
output = np.zeros_like(thresh)
cv2.drawContours(output, contours, -1, 255, -1)
for i, contour in enumerate(contours):
    x, y, w, h = cv2.boundingRect(contour)
    cv2.putText(output, f"{i}", (x, y), cv2.FONT_HERSHEY_PLAIN, 1, 255, 2)

这段代码的输出是这张图片：如您所见，只有一对单元格（轮廓#7）需要使用分水岭算法进行拆分。运行该单元格上的分水岭算法非常快（可以使用较小的图像），结果如下：

编辑一些可以用来评估分水岭算法是否应该运行在图像中的物体上的细胞形态学计算：

# area
area = cv2.contourArea(contour)
# perimeter, with the minimum value = 0.01 to avoid division by zero in other calculations
perimeter = max(0.01, cv2.arcLength(contour, True))
# circularity
circularity = (4 * math.pi * area) / (perimeter ** 2)
# Check if the cell is convex (not smoothly elliptical)
hull = cv2.convexHull(contour)
convexity = cv2.arcLength(hull, True) / perimeter
approx = cv2.approxPolyDP(contour, 0.1 * perimeter, True)
convex = cv2.isContourConvex(approx)

您需要找到项目中每个度量的阈值。在我的项目中，细胞是椭圆形的，有一个大面积凸起的斑点通常意味着有 2 个或更多细胞聚集在一起。

如何改进 Python 中异质结构的分水岭分割？

How can I improve Watershed segmentation of heterogenous structures in Python?

python

opencv

image-segmentation

watershed