计数图像中的细胞数

Question

我需要代码来计数图像中的细胞数量，并且只应计数粉红色的细胞。我使用了阈值和分水岭方法。

import cv2
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import numpy as np
import imutils

image = cv2.imread("cellorigin.jpg")

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,
    cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
cv2.imshow("Thresh", thresh)


D = ndimage.distance_transform_edt(thresh)
localMax = peak_local_max(D, indices=False, min_distance=20,
    labels=thresh)
cv2.imshow("D image", D)

markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]
labels = watershed(-D, markers, mask=thresh)
print("[INFO] {} unique segments found".format(len(np.unique(labels)) -     1))

for label in np.unique(labels):
    # if the label is zero, we are examining the 'background'
    # so simply ignore it
    if label == 0:
        continue

    # otherwise, allocate memory for the label region and draw
    # it on the mask
    mask = np.zeros(gray.shape, dtype="uint8")
    mask[labels == label] = 255

    # detect contours in the mask and grab the largest one
    cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
        cv2.CHAIN_APPROX_SIMPLE)
    cnts = imutils.grab_contours(cnts)
    c = max(cnts, key=cv2.contourArea)

    # draw a circle enclosing the object
    ((x, y), r) = cv2.minEnclosingCircle(c)
    cv2.circle(image, (int(x), int(y)), int(r), (0, 255, 0), 2)
    cv2.putText(image, "#{}".format(label), (int(x) - 10, int(y)),
        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)



cv2.imshow("input",image

cv2.waitKey(0)

我无法正确分割粉红色单元格。在某些地方，两个粉红色单元格连接在一起，也应分开。

Answer 1

由于细胞的可见性似乎不同于细胞核（深紫色）和背景（浅粉红色），因此此处应使用颜色阈值。想法是将图像转换为HSV格式，然后使用上下颜色阈值隔离细胞。这将为我们提供一个二进制掩码，我们可以使用它来计数单元格的数量。

---

我们首先将图像转换为HSV格式，然后使用较低/较高的颜色阈值创建二进制掩码。从这里开始，我们执行形态学操作以平滑图像并去除少量噪声。

现在我们有了遮罩，我们找到了带有cv2.RETR_EXTERNAL参数的轮廓，以确保仅采用外部轮廓。我们定义了几个面积阈值以滤除单元格

minimum_area = 200
average_cell_area = 650
connected_cell_area = 1000

该minimum_area阈值确保我们不计算单元格的微小部分。由于某些单元是连接的，因此某些轮廓可能会将多个连接的单元表示为单个轮廓，因此为了更好地估计单元，我们定义了一个average_cell_area参数来估计单个单元的面积。该connected_cell_area参数检测连接的单元格，math.ceil()并在连接的单元格轮廓上使用估计该轮廓中的单元格数量。要计算单元格的数量，我们遍历轮廓并根据其面积对轮廓进行汇总。这是检测到的单元格以绿色突出显示

Cells: 75

码

import cv2
import numpy as np
import math

image = cv2.imread("1.jpg")
original = image.copy()
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

hsv_lower = np.array([156,60,0])
hsv_upper = np.array([179,115,255])
mask = cv2.inRange(hsv, hsv_lower, hsv_upper)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
opening = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=1)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=2)

cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

minimum_area = 200
average_cell_area = 650
connected_cell_area = 1000
cells = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area > minimum_area:
        cv2.drawContours(original, [c], -1, (36,255,12), 2)
        if area > connected_cell_area:
            cells += math.ceil(area / average_cell_area)
        else:
            cells += 1
print('Cells: {}'.format(cells))
cv2.imshow('close', close)
cv2.imshow('original', original)
cv2.waitKey()