How can local maximum filtering be used to identify pressure peaks in a 2D array representing a dog\'s paw?-Python Tutorial-php.cn

How can local maximum filtering be used to identify pressure peaks in a 2D array representing a dog\'s paw?

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Release： 2024-11-04 09:25:30

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How can local maximum filtering be used to identify pressure peaks in a 2D array representing a dog's paw?

Peak Detection in a 2D Array

Challenge:

Detecting peaks in a 2D array representing pressure measurements under a dog's paw, to delineate anatomical subregions.

Solution:

The practical solution involves using a local maximum filter to identify peaks. Here's how:

<code class="python">import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import generate_binary_structure, binary_erosion

# Define the paw data
paw_data = np.loadtxt("paws.txt").reshape(4, 11, 14)

# Define the 8-connected neighborhood
neighborhood = generate_binary_structure(2, 2)

# Function to detect peaks
def detect_peaks(image):
    # Local maximum filter
    local_max = maximum_filter(image, footprint=neighborhood) == image
    
    # Create a mask of the background
    background = (image == 0)
    
    # Erode the background to remove artifacts
    eroded_background = binary_erosion(background, structure=neighborhood, border_value=1)
    
    # Final mask containing only peaks
    detected_peaks = local_max ^ eroded_background
    
    return detected_peaks

# Detect peaks for each paw
paws = [p.squeeze() for p in np.vsplit(paw_data, 4)]
detected_peaks_list = []
for paw in paws:
    detected_peaks = detect_peaks(paw)
    detected_peaks_list.append(detected_peaks)

# Plot the results
fig, axs = plt.subplots(4, 2, figsize=(10, 10))
for i, paw in enumerate(paws):
    axs[i, 0].imshow(paw)
    axs[i, 0].set_title("Paw Image")
    axs[i, 1].imshow(detected_peaks_list[i])
    axs[i, 1].set_title("Peak Detection")

plt.tight_layout()
plt.show()</code>

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Considerations:

This approach assumes a clean background and may not be suitable for noisy data.
The neighborhood size may need to be adjusted based on the peak size.
Further analysis can involve using scipy.ndimage.measurements.label to label distinct objects (peaks).

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