Minimum Cross-Entropy Image Segmentation Source Code (Original Implementation)

Minimum cross-entropy image segmentation is an information theory-based image segmentation method that identifies optimal segmentation thresholds by calculating the cross-entropy between image pixel grayscale distribution and segmentation thresholds. This method effectively separates targets from background in complex scenarios, making it suitable for medical imaging, industrial inspection, and various other applications. The core implementation typically involves calculating grayscale histograms and minimizing the cross-entropy objective function through optimization algorithms.

The core concept utilizes cross-entropy as a loss function, where optimization algorithms find the threshold that minimizes this loss. Cross-entropy measures the divergence between actual pixel distribution and ideal segmented distribution. Optimizing this metric ensures strong intra-region consistency while maximizing inter-region distinguishability. In code implementation, this translates to defining an objective function that takes candidate thresholds as input and returns corresponding cross-entropy values.

Algorithm implementation generally includes these key steps: Compute the image grayscale histogram to quantify frequency distribution across grayscale levels. In MATLAB, this can be implemented using imhist() function or manual histogram calculation through histcounts(). Define the cross-entropy objective function that accepts candidate thresholds as input and outputs cross-entropy values for current threshold. The function typically calculates entropy differences between foreground and background distributions. Employ search strategies (exhaustive search, gradient descent, or evolutionary algorithms) to find thresholds minimizing the objective function. Code implementation may use fminbnd() for bounded optimization or custom iterative search loops. Apply the optimal threshold for image binarization segmentation using imbinarize() with custom threshold or direct logical indexing operations.

Compared to traditional Otsu's method, this approach demonstrates more stable performance in low-contrast or non-uniform illumination conditions. The optimization process can incorporate adaptive strategies for multi-threshold segmentation problems, or integrate spatial information to enhance noise resistance through techniques like morphological operations or spatial continuity constraints in the objective function.