Balanced histogram thresholding
In image processing, the balanced histogram thresholding method, is a very simple method used for automatic image thresholding. Like Otsu's Method and the Iterative Selection Thresholding Method, this is a histogram based thresholding method. This approach assumes that the image is divided in two main classes: The background and the foreground. The BHT method tries to find the optimum threshold level that divides the histogram in two classes.
This method weighs the histogram, checks which of the two sides is heavier, and removes weight from the heavier side until it becomes the lighter. It repeats the same operation until the edges of the weighing scale meet.
Given its simplicity, this method is a good choice as a first approach when presenting the subject of automatic image thresholding.
Algorithm
The following listing, in C notation, is a simplified version of the Balanced Histogram Thresholding method:int BHThreshold
The following, is a possible implementation in the Python language:
def balanced_histogram_thresholding -> int:
"""
Determines an optimal threshold by balancing the histogram of an image,
focusing on significant histogram bins to segment the image into two parts.
Args:
histogram : The histogram of the image as a list of integers,
where each element represents the count of pixels
at a specific intensity level.
minimum_bin_count : Minimum count for a bin to be considered in the
thresholding process. Bins with counts below this
value are ignored, reducing the effect of noise.
jump : Step size for adjusting the threshold during iteration. Larger values
speed up convergence but may skip the optimal threshold.
Returns:
int: The calculated threshold value. This value represents the intensity level
that best separates the significant
parts of the histogram into two groups, which can be interpreted as foreground
and background.
If the function returns -1, it indicates that the algorithm was unable to find
a suitable threshold within the constraints.
"""
# Find the start and end indices where the histogram bins are significant
start_index = 0
while start_index < len and histogram < minimum_bin_count:
start_index += 1
end_index = len - 1
while end_index >= 0 and histogram < minimum_bin_count:
end_index -= 1
# Check if no valid bins are found
if start_index >= end_index:
return -1 # Indicates an error or non-applicability
# Initialize threshold
threshold = // 2
# Iteratively adjust the threshold
while start_index <= end_index:
# Calculate weights on both sides of the threshold
weight_left = sum
weight_right = sum
# Adjust the threshold based on the weights
if weight_left > weight_right:
start_index += jump
elif weight_left < weight_right:
end_index -= jump
else: # Equal weights; move both indices
start_index += jump
end_index -= jump
# Calculate the new threshold
threshold = // 2
return threshold