Python粒子图像分析：检测过多微小区域的问题排查

背景与需求 #

• 正在从ImageJ转向使用Python处理粒子图像（涵盖台面上的静态粒子或下落中的动态粒子），已编写首版代码实现以下功能：
  1. 读取粒子图像
  2. 通过阈值分割与降噪生成二值图像
  3. 应用分水岭算法分离重叠粒子
  4. 检测区域并测量，获取粒径分布信息（如面积、周长、轴长等）
• 当前测试样本为台面上粒径0.5-1mm的粒子图像；后续还需处理粒径0.25-0.5mm、需要使用分水岭算法的粒子图像

问题现象 #

• 运行Python代码后得到的粒子面积分布中，0.0-0.5mm²区间存在大量实际不存在的微小区域
• 使用ImageJ处理同一样本则无此问题

已尝试措施 #

调整阈值分割、降噪、形态学操作及区域测量的相关参数，问题始终未解决

代码实现 #

import cv2
import numpy as np
import pandas as pd
import os
from skimage.segmentation import watershed, clear_border
from skimage import measure, color, io, morphology

# Define the folder path containing the images
image = "C:/..."

# Set scale pixels/mm (either based on ImageJ or by reference image, or calculations)
px_per_mm = 222
mm_per_px = 1 / px_per_mm
img = cv2.imread(image)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#Threshold and remove noise
thresholded_img =  cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

kernel = np.ones((5, 5),np.uint8)
image = cv2.morphologyEx(thresholded_img, cv2.MORPH_OPEN, kernel)
image = clear_border(image)

# Prepare for Watershed
sure_bg = cv2.dilate(image,kernel, iterations=4)
dist_transform = cv2.distanceTransform(image, cv2.DIST_L2, 0)
ret2, sure_fg = cv2.threshold(dist_transform, 0.4 * dist_transform.max(), 255, 0)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
ret3, markers = cv2.connectedComponents(sure_fg, connectivity=8)
markers = markers + 10
markers[unknown == 255] = 0

# Now we are ready for watershed filling.
markers = cv2.watershed(img, markers)

#Measure properties
label_image = measure.label(markers,background=255, connectivity=None)
props = measure.regionprops_table(label_image, image,
                          properties=['label', 'Area'])

# Scale properties from pixels to mm
props['Area'] = props['Area'] * mm_per_px ** 2

# Remove the image frame that is usually identified as a region
max_area_threshold = 700
props_df = pd.DataFrame(props)
filtered_props = props_df[props_df['Area'] <= max_area_threshold]
# Convert the filtered properties to a DataFrame
particle_analysis = pd.DataFrame(filtered_props)

内容的提问来源于stack exchange，提问作者Ant_C