Statistical Threshold Selection: Using the Percentile Method in Image Segmentation

In this video, we continue our deep dive into Global Thresholding for Image Segmentation. Delivered in Urdu/Hindi, this lecture focuses on a more robust statistical method for automatically selecting the optimal threshold point (T): the Percentile Method. What you will learn in this video: Motivation for Statistical Thresholding: Why the mean and median methods fail when the foreground object occupies a very small or very large percentage of the total image area. The Idea Behind Percentiles: Understanding how percentiles relate to the Cumulative Distribution Function (CDF) of the image intensity values. Choosing the Cutoff Point: Discussion on the idea of using the known percentage of the object in the image to inform the threshold selection (e.g., if a tumor is known to occupy 5% of the scan area, we can use the 95th percentile as a starting threshold). Percentile Method Algorithm: A step-by-step explanation of the procedure for finding the threshold T such that a specified percentage of the pixels fall below it. Practical Advantage: Why the percentile method is a much more adaptive and reliable choice than simple averaging when dealing with images where the object-to-background ratio is unbalanced. Note: While the percentile method is better than the mean/median, it still requires prior knowledge about the object size. Future videos will cover fully automated thresholding techniques, such as Otsu's method. Hashtags #ImageProcessing #ImageSegmentation #GlobalThresholding #PercentileMethod #StatisticalThresholding #CDF #UrduTutorial #HindiTutorial