diff --git a/computer_vision/intensity-based_segmentation.py b/computer_vision/intensity-based_segmentation.py
new file mode 100644
index 000000000000..f32bba6d17aa
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+++ b/computer_vision/intensity-based_segmentation.py
@@ -0,0 +1,62 @@
+# Importing necessary libraries
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from typing import List
+
+def segment_image(image: np.ndarray, thresholds: List[int]) -> np.ndarray:
+    """
+    Performs image segmentation based on intensity thresholds.
+    Args:
+        image (np.ndarray): Input grayscale image as a 2D array.
+        thresholds (List[int]): A list of intensity thresholds to define segments.
+    Returns:
+        np.ndarray: A labeled 2D array where each region corresponds to a threshold range.
+    Example:
+    >>> img = np.array([[80, 120, 180], [40, 90, 150], [20, 60, 100]])
+    >>> segment_image(img, [50, 100, 150])
+    array([[1, 2, 3],
+           [0, 1, 2],
+           [0, 0, 1]])
+    """
+    # Initialize an empty array to store segment labels
+    segmented = np.zeros_like(image, dtype=np.int32)
+
+    # Iterate over thresholds and label pixels in corresponding intensity ranges
+    for i, threshold in enumerate(thresholds):
+        segmented[image > threshold] = i + 1
+
+    return segmented
+
+if __name__ == "__main__":
+    # Path to the image file
+    image_path = "path_to_image"  # Replace with the path to your local image file
+
+    # Load and preprocess the image
+    original_image = Image.open(image_path).convert("L")  # Convert image to grayscale
+    image_array = np.array(original_image)  # Convert image to a numpy array
+
+    # Specify intensity thresholds for segmentation
+    thresholds = [50, 100, 150, 200]  # Define your desired thresholds
+
+    # Apply segmentation to the image
+    segmented_image = segment_image(image_array, thresholds)
+
+    # Visualize the results
+    plt.figure(figsize=(12, 6))
+
+    # Display the original image
+    plt.subplot(1, 2, 1)
+    plt.title("Original Grayscale Image")
+    plt.imshow(image_array, cmap="gray")
+    plt.axis("off")
+
+    # Display the segmented image with labeled regions
+    plt.subplot(1, 2, 2)
+    plt.title("Segmented Image")
+    plt.imshow(segmented_image, cmap="tab20")  # Use a colormap for better distinction
+    plt.axis("off")
+
+    # Show the plots
+    plt.tight_layout()
+    plt.show()