Optimized Otsu Image Segmentation Threshold Algorithm MATLAB Source Code

In image processing, the Otsu segmentation threshold algorithm is widely used for partitioning images into distinct regions. While this algorithm proves highly effective, its computational efficiency can present challenges. To address this limitation, we have developed an optimized version of the Otsu algorithm that substantially reduces computation time. The implementation leverages MATLAB's vectorization capabilities and employs a probability density function approach using image histograms. Instead of testing all possible thresholds through enumeration, the optimized algorithm calculates between-class variance using cumulative probability distributions, reducing complexity from O(L²) to O(L) where L represents the intensity levels. Key functions include histogram calculation using imhist(), probability distribution computation, and iterative variance maximization. The core algorithm implementation involves: 1. Precomputing image histogram and probability distribution 2. Calculating cumulative sums for efficient variance computation 3. Implementing an optimized search for threshold that maximizes between-class variance Compared to traditional enumeration-based Otsu methods, our optimized approach demonstrates significant advantages in computational speed while maintaining the same optimal threshold determination accuracy. This optimization enables more efficient image segmentation processing while conserving valuable computational resources. The MATLAB source code implementation provides a practical solution for real-time image processing applications where computational efficiency is crucial.