Research on Two-Dimensional Entropy Threshold Method Based on Improved Genetic Algorithm

Image segmentation has always been the difficulty and focus in image processing research. The traditional best histogram threshold method is not ideal for segmentation of images. This paper presents a modified method of two-dimensional entropy threshold method and its improved genetic algorithm. Compared with the traditional entropy threshold method, the two-dimensional entropy threshold method not only reflects the gray-level distribution information, but also reflects the image pixel neighborhood spatial correlation information, greatly improving the image segmentation effect. At the same time, the improved genetic algorithm is used to solve the problem that the traditional genetic algorithm has slow convergence and improper threshold selection. This algorithm improves the threshold search speed and effect.