Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram

Abstract The traditional fuzzy entropy segmentation method is based on 1D histogram which cannot reflect spatial information and is affected by noise acutely. In this paper a new definition of 2D histogram is proposed. Different images are divided into grids of different densities, and then the gray value of each pixel in the image and the average gray value of the grid which the pixel is located are used as two components of the 2D histogram. To get thresholds value which can be used to segment the image into three levels, a new multi-threshold image segmentation method using maximum fuzzy entropy based on the proposed 2D histogram is presented. The parameters of the fuzzy entropy function can be determined by genetic algorithm with an appropriate coding method. The experiment results show that by using different grids of proper density to divide different images, the proposed method obtains better performance for target information extraction.

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