What is intensity level slicing?

Intensity level slicing is a fundamental technique used in digital image processing. This method involves identifying and highlighting a specific range of pixel intensities within an image, enabling enhanced visualization and extracting valuable information.

Imagine a grayscale MRI scan where medical professionals are using intensity level slicing to detect potential brain tumors. In this scenario, intensity level slicing involves setting a threshold to highlight abnormal intensity levels, effectively emphasizing regions that may indicate the presence of a tumor. This example highlights the role of intensity level slicing in the precise analysis and early detection of abnormalities.

Now, let’s explore how this process actually works.

How does it work

There are two ways in which we can perform intensity level slicing on grayscale images.

Conversion to a binary image

In binary images, intensity level slicing involves setting a threshold value to distinguish between these two intensity levels. One approach is to display the desired intensity range in 1 while suppressing all other intensities to 0, resulting in a binary image. This is achieved by applying a transformation function that selectively assigns 1 to the desired range and 0 to the rest of the intensities or vice versa. This method effectively isolates and emphasizes specific regions based on their intensity values in such images.

def intensity_level_slicing(image, lower_threshold, upper_threshold):
    """
    Apply intensity level slicing to a grayscale image.
    Parameters:
    image (list of list of int): Input grayscale image.
    lower_threshold (int): Lower threshold of intensity range.
    upper_threshold (int): Upper threshold of intensity range.
    Returns:
    list of list of int: Binary image after intensity level slicing.
    """
    # Initialize an empty list to store the binary image
    binary_image = []
    
    # Iterate over each row in the input grayscale image
    for row in image:
        # Initialize an empty list to store the binary values for the current row
        binary_row = []
        
        # Iterate over each pixel in the current row
        for pixel in row:
            # Check if the pixel value is within the specified intensity range
            if lower_threshold <= pixel <= upper_threshold:
                # If within the range, set the corresponding binary value to 1
                binary_row.append(1)  # Set to 1
            else:
                # If outside the range, set the corresponding binary value to 0
                binary_row.append(0)  # Set to 0
        
        # Append the binary row to the binary image
        binary_image.append(binary_row)
    
    # Return the binary image
    return binary_image
def print_image(image):
    """
    Print the image to the console.
    Parameters:
    image (list of list of int): The image to print.
    """
    # Iterate over each row in the image
    for row in image:
        # Join the pixel values in the row into a single string with spaces in between
        # and print the resulting string
        print(' '.join(str(pixel) for pixel in row))
# Example grayscale image represented as a 2D list
image = [
    [50, 100, 150, 200],
    [80, 120, 160, 210],
    [90, 130, 170, 220],
    [70, 110, 140, 190]
]
# Define intensity range for slicing
lower_threshold = 100
upper_threshold = 200
# Apply intensity level slicing
binary_image = intensity_level_slicing(image, lower_threshold, upper_threshold)
# Print the original and binary images
print("Original Image:")
print_image(image)
print("\nBinary Image:")
print_image(binary_image)

In this example, first, all the pixel intensities that fall within the given range are highlighted. Then, these highlighted pixels are brightened by 10 values while the rest are left unchanged.

Applications

Let’s explore some uses of intensity level slicing.

Medical imaging
1. Tumor detection: Highlighting specific intensity levels in medical images like MRI and CT scans to identify tumors or anomalies with different tissue densities.
2. Enhanced visualization: Enhancing the visibility of specific structures or tissues for diagnostic purposes, for example highlighting bones of an X-ray image.
Satellite and remote sensing
1. Vegetation analysis: Identifying and analyzing different types of vegetation or land cover in satellite images.
2. Fire detection: Detecting areas with intense heat signatures, such as forest fires, in thermal imaging data.
Security and surveillance
1. Object detection: Isolating and highlighting moving objects or individuals in security camera footage based on their intensity or heat signatures.
2. Intrusion detection: Detecting intruders by tracking their movements within a frame.
Art and photography
1. Special effects: Creating artistic effects in photography by emphasizing specific colors or shades.
2. Contrast enhancement: Enhancing contrast and details in photos for visual appeal.
Industrial quality control
1. Defect detection: Highlighting defects or inconsistencies in products during manufacturing to ensure quality control.
2. Surface inspection: Examining surfaces for anomalies, such as cracks or imperfections.

Conclusion

Intensity level slicing has applications spanning various domains. It helps with the early detection of anomalies in medical imaging and highlights specific features in satellite and remote sensing data. Additionally, it contributes to security and surveillance through object detection and enhances industrial quality control. It plays an important role in extracting meaningful information from images, providing valuable insights across diverse fields, and contributing to advancements in technology and analysis.

What is intensity level slicing?

How does it work

Conversion to a binary image

Highlighting selected pixel range

Applications

Conclusion