Two-Dimensional Maximum Entropy Grayscale Image Segmentation

Two-dimensional maximum entropy grayscale image segmentation is an image processing method based on information entropy theory that determines the optimal segmentation threshold by comprehensively considering both the pixel's gray value and the average gray value of its neighborhood. Compared to one-dimensional maximum entropy methods, the 2D approach demonstrates superior performance in handling noisy images and those with uneven gray-level distributions, thereby improving segmentation accuracy. In implementation, this typically involves calculating local mean values using convolution operations with neighborhood windows.

In practical applications, the 2D maximum entropy method first computes each pixel's gray value and its neighborhood's average gray value to construct a two-dimensional histogram. The algorithm then searches for the optimal threshold combination by maximizing the entropy of this 2D histogram, effectively dividing the image into foreground and background regions. This approach can be implemented through nested loops that iterate through possible threshold pairs, calculating entropy values for each combination. The method not only effectively suppresses noise interference but also adapts to image segmentation requirements under varying illumination conditions through its dual-threshold optimization mechanism.

Two-dimensional maximum entropy grayscale image segmentation is widely applied in medical image analysis, industrial inspection, and remote sensing image processing. The algorithm's core function involves entropy calculation using probability distributions from the 2D histogram, making it a stable and efficient segmentation technique suitable for integration into image processing libraries like OpenCV or MATLAB's Image Processing Toolbox.