An expert system based on fuzzy entropy for automatic threshold selection in image processing

In pattern recognition and image processing, the selection of appropriate threshold is a very significant issue. Especially, the selecting gray-level thresholds is a critical issue for many pattern recognition applications. Here, the maximum fuzzy entropy and fuzzy c-partition methods are used for the aim of the gray-level automatic threshold selection method. The fuzzy theory has been successfully applied to many areas, such as image processing, pattern recognition, computer vision, medicine, control, etc. The images have some fuzziness in nature. In this study, expert maximum fuzzy-Sure entropy (EMFSE) method for the maximum fuzzy entropy and fuzzy c-partition processes in automatic threshold selection is proposed. The experimental studies were conducted on many images by testing maximum fuzzy-Sure entropy against maximum fuzzy-Shannon entropy (MFSHE), maximum fuzzy-Havrada and Charvat entropy (MFHCE) methods for selecting optimum 2-level threshold value, respectively. The obtained experimental results show that the used MFSE method is superior to other MFSHE and MFHCE methods on selecting the 2-level threshold value automatically and effectively.