python - 提高图像处理的准确性以计算真菌孢子

Question

我正在尝试使用 Pythony 计算显微镜样本中疾病孢子的数量，但到目前为止没有取得太大成功。

因为孢子的颜色和背景很相似，很多都很接近。

根据 sample 的显微照相。

图像处理代码:

import numpy as np
import argparse
import imutils
import cv2

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to the input image")
ap.add_argument("-o", "--output", required=True,
                help="path to the output image")
args = vars(ap.parse_args())

counter = {}

image_orig = cv2.imread(args["image"])
height_orig, width_orig = image_orig.shape[:2]

image_contours = image_orig.copy()

colors = ['Yellow']
for color in colors:

    image_to_process = image_orig.copy()

    counter[color] = 0

    if color == 'Yellow':
        lower = np.array([70, 150, 140])  #rgb(151, 143, 80)
        upper = np.array([110, 240, 210])  #rgb(212, 216, 106)

    image_mask = cv2.inRange(image_to_process, lower, upper)

    image_res = cv2.bitwise_and(
        image_to_process, image_to_process, mask=image_mask)

    image_gray = cv2.cvtColor(image_res, cv2.COLOR_BGR2GRAY)
    image_gray = cv2.GaussianBlur(image_gray, (5, 5), 50)

    image_edged = cv2.Canny(image_gray, 100, 200)
    image_edged = cv2.dilate(image_edged, None, iterations=1)
    image_edged = cv2.erode(image_edged, None, iterations=1)

    cnts = cv2.findContours(
        image_edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    cnts = cnts[0] if imutils.is_cv2() else cnts[1]

    for c in cnts:

        if cv2.contourArea(c) < 1100:
            continue

        hull = cv2.convexHull(c)
        if color == 'Yellow':

            cv2.drawContours(image_contours, [hull], 0, (0, 0, 255), 1)

        counter[color] += 1      

print("{} esporos {}".format(counter[color], color))

cv2.imwrite(args["output"], image_contours)

算法统计了11 spores

但图中包含27个孢子

图像处理结果显示孢子被分组

如何使它更准确？

Answer 1

首先，我们将在下面使用一些初步代码:

import numpy as np
import cv2
from matplotlib import pyplot as plt
from skimage.morphology import extrema
from skimage.morphology import watershed as skwater

def ShowImage(title,img,ctype):
  if ctype=='bgr':
    b,g,r = cv2.split(img)       # get b,g,r
    rgb_img = cv2.merge([r,g,b])     # switch it to rgb
    plt.imshow(rgb_img)
  elif ctype=='hsv':
    rgb = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
    plt.imshow(rgb)
  elif ctype=='gray':
    plt.imshow(img,cmap='gray')
  elif ctype=='rgb':
    plt.imshow(img)
  else:
    raise Exception("Unknown colour type")
  plt.title(title)
  plt.show()

作为引用，这是您的原始图片:

#Read in image
img         = cv2.imread('cells.jpg')
ShowImage('Original',img,'bgr')

Otsu's method是一种分割颜色的方法。该方法假设图像像素的强度可以绘制成双峰直方图，并找到该直方图的最佳分隔符。我应用下面的方法。

#Convert to a single, grayscale channel
gray        = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
#Threshold the image to binary using Otsu's method
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
ShowImage('Grayscale',gray,'gray')
ShowImage('Applying Otsu',thresh,'gray')

所有这些小 Blob 都很烦人，我们可以通过扩张来去除它们:

#Adjust iterations until desired result is achieved
kernel  = np.ones((3,3),np.uint8)
dilated = cv2.dilate(thresh, kernel, iterations=5)
ShowImage('Dilated',dilated,'gray')

我们现在需要识别分水岭的峰值并给它们单独的标签。这样做的目的是生成一组像素，使得每个单元格中都有一个像素，并且没有两个单元格的标识符像素接触。

为此，我们执行距离变换，然后过滤掉距离单元格中心太远的距离。

#Calculate distance transformation
dist         = cv2.distanceTransform(dilated,cv2.DIST_L2,5)
ShowImage('Distance',dist,'gray')

#Adjust this parameter until desired separation occurs
fraction_foreground = 0.6
ret, sure_fg = cv2.threshold(dist,fraction_foreground*dist.max(),255,0)
ShowImage('Surely Foreground',sure_fg,'gray')

就算法而言，上图中的每个白色区域都是一个单独的单元格。

现在我们通过减去最大值来识别未知区域，这些区域将被分水岭算法标记:

# Finding unknown region
unknown = cv2.subtract(dilated,sure_fg.astype(np.uint8))
ShowImage('Unknown',unknown,'gray')

未知区域应在每个单元格周围形成完整的 donut 。

接下来，我们为距离变换产生的每个不同区域赋予唯一标签，然后在最终执行分水岭变换之前标记未知区域:

# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg.astype(np.uint8))
ShowImage('Connected Components',markers,'rgb')

# Add one to all labels so that sure background is not 0, but 1
markers = markers+1

# Now, mark the region of unknown with zero
markers[unknown==np.max(unknown)] = 0

ShowImage('markers',markers,'rgb')

dist    = cv2.distanceTransform(dilated,cv2.DIST_L2,5)
markers = skwater(-dist,markers,watershed_line=True)

ShowImage('Watershed',markers,'rgb')

现在细胞总数是唯一标记的数量减去 1(忽略背景):

len(set(markers.flatten()))-1

在这种情况下，我们得到 23。

您可以通过调整距离阈值、扩张程度，或者使用 h-maxima(局部阈值最大值)来提高或降低准确度。但要小心过度拟合；也就是说，不要假设对单个图像进行调整会在任何地方为您提供最佳结果。

估计不确定性

您还可以通过算法稍微改变参数，以了解计数的不确定性。可能看起来像这样

import numpy as np
import cv2
import itertools
from matplotlib import pyplot as plt
from skimage.morphology import extrema
from skimage.morphology import watershed as skwater

def CountCells(dilation=5, fg_frac=0.6):
  #Read in image
  img         = cv2.imread('cells.jpg')
  #Convert to a single, grayscale channel
  gray        = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
  #Threshold the image to binary using Otsu's method
  ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
  #Adjust iterations until desired result is achieved
  kernel  = np.ones((3,3),np.uint8)
  dilated = cv2.dilate(thresh, kernel, iterations=dilation)
  #Calculate distance transformation
  dist         = cv2.distanceTransform(dilated,cv2.DIST_L2,5)
  #Adjust this parameter until desired separation occurs
  fraction_foreground = fg_frac
  ret, sure_fg = cv2.threshold(dist,fraction_foreground*dist.max(),255,0)
  # Finding unknown region
  unknown = cv2.subtract(dilated,sure_fg.astype(np.uint8))
  # Marker labelling
  ret, markers = cv2.connectedComponents(sure_fg.astype(np.uint8))
  # Add one to all labels so that sure background is not 0, but 1
  markers = markers+1
  # Now, mark the region of unknown with zero
  markers[unknown==np.max(unknown)] = 0    
  markers = skwater(-dist,markers,watershed_line=True)
  return len(set(markers.flatten()))-1

#Smaller numbers are noisier, which leads to many small blobs that get
#thresholded out (undercounting); larger numbers result in possibly fewer blobs,
#which can also cause undercounting.
dilations = [4,5,6] 
#Small numbers equal less separation, so undercounting; larger numbers equal
#more separation or drop-outs. This can lead to over-counting initially, but
#rapidly to under-counting.
fracs     = [0.5, 0.6, 0.7, 0.8] 

for params in itertools.product(dilations,fracs):
  print("Dilation={0}, FG frac={1}, Count={2}".format(*params,CountCells(*params)))

给出结果:

Dilation=4, FG frac=0.5, Count=22
Dilation=4, FG frac=0.6, Count=23
Dilation=4, FG frac=0.7, Count=17
Dilation=4, FG frac=0.8, Count=12
Dilation=5, FG frac=0.5, Count=21
Dilation=5, FG frac=0.6, Count=23
Dilation=5, FG frac=0.7, Count=20
Dilation=5, FG frac=0.8, Count=13
Dilation=6, FG frac=0.5, Count=20
Dilation=6, FG frac=0.6, Count=23
Dilation=6, FG frac=0.7, Count=24
Dilation=6, FG frac=0.8, Count=14

取计数值的中位数是将不确定性纳入单个数字的一​​种方法。

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