Dual Application of Otsu's Method for Image Segmentation

During the image segmentation process, we applied Otsu's method twice for enhanced separation. This dual-threshold approach first distinguishes foreground from background components, then specifically isolates white regions from the remaining background elements. The algorithm works by automatically calculating optimal threshold values that maximize inter-class variance while minimizing intra-class variance. We subsequently performed binarization to convert the image into a binary format (typically 0s and 1s or 0-255 pixel values) using the determined thresholds. This combination of techniques - double Otsu thresholding followed by binarization - enables clearer image interpretation and facilitates more effective subsequent analysis and processing. The implementation typically involves calculating histograms, determining optimal thresholds using Otsu's variance maximization formula, and applying cv2.threshold() functions in OpenCV or equivalent methods in other imaging libraries.