Identifying Largest Connected Component in Binary Images

This function first converts the input grayscale image to a binary image, then uses the measure module from the skimage library to label all connected components in the image. It then calculates the area of each component, finds the largest component, and finally returns the region of this largest component.

Technology Stack : scikit-image

Code Type : Image processing

Code Difficulty : Intermediate

                
                    
import numpy as np
from skimage import measure, color

def find_largest_connected_component(image):
    """
    This function finds the largest connected component in a binary image.
    """
    # Convert image to binary
    binary_image = image > 0.5
    
    # Label all the connected components in the image
    labeled_image, num_features = measure.label(binary_image)
    
    # Find the area of each connected component
    regions = measure.regionprops(labeled_image)
    
    # Find the largest connected component
    largest_component = max(regions, key=lambda r: r.area)
    
    # Extract the region of the largest component
    x0, y0, x1, y1 = largest_component.bbox
    largest_component_image = binary_image[y0:y1, x0:x1]
    
    return largest_component_image