Image segmentation using clusterization

Threshold based methods are the most frequently used methods of image segmentation due to their intuitive nature and ease of implementation. In this article, we consider methods of automatic threshold selection, as well as methods for threshold adjustment in accordance with local image properties. The studies were conducted using Matlab software. Global threshold processing cannot produce the desired result if the image background is highly heterogeneous in brightness. In such cases, it is necessary to apply pre-processing to compensate for variations in background brightness, after which a global threshold transformation can be performed. One way to choose a threshold is to visually examine the image histogram. If the histogram has two distinct modes, then it is easy to choose the threshold separating them. Another approach to threshold selection is based on the trial and error method, when different thresholds are selected and checked until the result of the threshold processing satisfies the requirements.