Calculating Optimal Image Threshold in MATLAB

This article focuses on determining optimal image thresholds through computational methods. To better comprehend this concept, we can employ different image processing algorithms such as Otsu's method (also known as the maximum variance thresholding method) to calculate the optimal threshold value. In MATLAB implementation, this typically involves using functions like graythresh() which automatically computes the threshold using Otsu's algorithm based on the image's histogram distribution. The algorithm works by maximizing the inter-class variance between foreground and background pixels, ensuring accurate segmentation during image processing. Additionally, we can explore other related techniques such as adaptive thresholding using adaptthresh() function or multi-level thresholding approaches to further optimize image processing results. These methods may involve analyzing local image characteristics or implementing iterative optimization algorithms to handle complex lighting conditions and improve segmentation precision.