skimage峰的局部最大值由于图像杂质而在附近找到多个斑点

Question

我有一个看起来像这样的图像，其中有一些较大的杂质/曝光过度的斑点。通常，检测到它们并不重要，因为测量是时间分辨的，因此稍后将其删除。

但是，我对尽可能多的小点感兴趣-尽可能快。skimage.feature.peak_local_max确实做得很好，并且非常容易在不同数据上使用，因为不需要在强度缩放上花太多时间。

问题是，由于某种原因，大斑点会产生非常强的正面影响。

import skimage.io

import skimage.feature

import skimage.morphology

from matplotlib.collections import PatchCollection

import matplotlib.pyplot as plt

def plotRoi(spots, img_ax, color, radius):

    patches = []

    for spot in spots:

        y, x = spot

        c = plt.Circle((x, y), radius)

        patches.append(c)

    img_ax.add_collection(PatchCollection(patches, facecolors = "None", edgecolors = color, alpha = 0.3, linewidths = 1))

img = skimage.io.imread("/Path/to/img.png")

img = img[:,:,0]

fig, ax = plt.subplots()

ax.imshow(img, cmap = "Greys")

spots = skimage.feature.peak_local_max(img, min_distance = 0, exclude_border = True, num_peaks = 2000)

plotRoi(spots, ax, "red", radius = 10)

plt.show()

在某些图像中搜索数千个斑点会导致大量局部最大值彼此重叠。有没有一种方法可以避免这种情况，例如在图像加载时应用滤镜，因为我不希望转向较慢的峰拟合？

Answer 1

问题在于，像素区域中的峰具有完全相同的值。解决此问题的一种方法是将峰合并到这些区域的质心。下面，我重新创建问题并按照说明解决它。import numpy as npimport matplotlib.pyplot as pltfrom skimage.feature import peak_local_maxfrom scipy.ndimage.measurements import center_of_mass, label# Generate test data with two peaks, one of which consists of two pixels of equal valueimage = np.zeros((11,11),dtype=np.uint8)image[5,3] = 128image[5,2] = 255image[5,7:9] = 255image[6,8] = 128# Finding peaks normally; results in three peakspeaks = peak_local_max(image)# Find peaks and merge equal regions; results in two peaksis_peak = peak_local_max(image, indices=False) # outputs bool imagelabels = label(is_peak)[0]merged_peaks = center_of_mass(is_peak, labels, range(1, np.max(labels)+1))merged_peaks = np.array(merged_peaks)# Visualize the resultsfig,(ax1,ax2)=plt.subplots(1,2)ax1.imshow(image.T,cmap='gray')ax1.plot(peaks[:,0],peaks[:,1],'ro')ax2.imshow(image.T,cmap='gray')ax2.plot(merged_peaks[:,0],merged_peaks[:,1],'ro')结果：