Efficient solution of Otsu multilevel image thresholding: A comparative study

Multi-level thresholding of a gray image is one of the basic operations in computer vision, with applications in image enhancement and segmentation. Various criteria for the selection of threshold level values were proposed. One of these criterion is the Otsu criterion that uses maximization of between-class variance approach. Although applying multi-level thresholding to an image is a straightforward operation, computation of the threshold levels with Otsu criterion is a computationally expensive process. In this paper, we revisit a dynamic programming algorithm that provides exact and efficient solution to the problem and compare it with modern meta-heuristic algorithms. We provide a rigorous proof for the correctness of the algorithm. The algorithm computational cost is linear in the number of threshold levels. We compare the algorithm with state of the art algorithms and verify its superior performance. The experiments show that we could gain speedup up to 2.45 × .