An efficient adaptive multilevel Renyi entropy thresholding method based on the energy curve with dynamic programming

Renyi entropy-based thresholding is a popular image segmentation method. In this work, to improve the performance of the Renyi entropy thresholding method, an efficient adaptive multilevel Renyi entropy thresholding method based on the energy curve with dynamic programming (DP + ARET) is presented. First, the histogram is substituted by the energy curve in the Renyi entropy thresholding to take advantage of the spatial context information of pixels. Second, an adaptive entropy index selection strategy is proposed based on the image histogram. Finally, to decrease the computation complexity of the multilevel Renyi entropy thresholding, an efficient solution is calculated by the dynamic programming technique. The proposed DP + ARET method can obtain the global optimal thresholds with the time complexity linear in the number of the thresholds. The comparative experiments between the proposed method with the histogram-based method verified the effectiveness of the energy curve. The segmentation results on the COVID-19 Computed Tomography (CT) images with the same objective function by the proposed DP + ARET and swarm intelligence optimization methods testify that the DP + ARET can quickly obtain the global optimal thresholds. Finally, the performance of the DP + ARET method is compared with several image segmentation methods quantitatively and qualitatively, the average segmented accuracy (SA) is improved by 7% than the comparative methods. The proposed DP + ARET method can be used to fast segment the images with no other prior knowledge.