Multilevel thresholding method based on immune genetic algorithm and Gaussian mixture model for image segmentation
基于IGA与GMM的图像多阈值分割方法

This paper proposed an adaptive multilevel thresholding algorithm based on immune genetic algorithm and Gaussian mixture model for image segmentation.The method allowed the determination of the appropriate number of thresholds as well as the adequate threshold values.The threshold candidate set with limited valley points could be attained by means of transforming the histogram with the right scale continuous wavelet.Then,determined the number of thresholds and the quasi-threshold values by using the immune genetic algorithm.The parameters of Gaussian mixture model could be received by the way of fitting the histogram with the quasi-threshold values.Last,the segmentation thresholds could be attained by using of the minimum error criterion.Simulation results show that this algorithm is very well in improving the speed,and good segmentation results can be received.