Automatic Threshold Segmentation Method Using Image Entropy

In image processing, threshold segmentation is a commonly used technique that divides an image into two or more regions for easier processing. Among various methods, automatic threshold segmentation using image entropy has been widely applied across multiple domains. This approach is not only straightforward to implement but also offers fast processing speeds, making it efficient for handling large-scale image datasets. The method determines optimal thresholds by calculating the entropy values of images, thereby achieving effective image segmentation. Implementation typically involves computing the probability distribution of pixel intensities and applying entropy maximization principles to select thresholds that maximize information separation between foreground and background regions. Key algorithmic steps include histogram calculation, probability distribution analysis, and entropy-based threshold optimization. Consequently, this technique shows broad application prospects in medical imaging, remote sensing, and other fields where automated image analysis is required.