Classic Otsu Thresholding Algorithm for Image Segmentation

The Otsu algorithm (OTSU) is a classical image thresholding segmentation method that automatically calculates the optimal threshold to separate an image into foreground and background. Its core principle is to maximize inter-class variance, ensuring maximum distinction between the two segmented pixel groups.

Algorithm implementation steps include: Compute image histogram: Count frequency occurrences for each grayscale level. Initialize parameters: Iterate through all possible threshold values, calculating foreground and background pixel proportions for each candidate threshold. Calculate inter-class variance: Combine mean differences between foreground/background classes with pixel proportions to derive variance values. Determine optimal threshold: Select the threshold maximizing inter-class variance as the final segmentation point.

MATLAB implementation typically involves these key operations: Using built-in functions like imhist() or histogram() for histogram computation Looping through all grayscale levels (0-255 for 8-bit images) Vectorized calculations for efficiency using array operations Final image binarization through threshold application with imbinarize() or direct logical indexing

This threshold-free algorithm adapts well to uneven illumination conditions, features low computational complexity with stable performance, and remains a benchmark method in image processing applications.