A FAST 2-DIMENSIONAL ENTROPIC THRESHOLDING ALGORITHM

Two-dimensional (2D) entropic thresholding is one of the important thresholding techniques for image segmentation, The selection of the global threshold vector is usually through a ''maximin'' optimization procedure. A fast two-phase 2D entropic thresholding algorithm is proposed. In order to reduce the computation time, first 9L2/3 candidate threshold vectors are estimated from a quantized image of the original. The global threshold vector is then obtained by checking candidates only. The optimal computation complexity is O(L8/3) by quantizing the gray level into L2/3 levels. Experimental results show that the processing time of each image is reduced from more than 2 h to about 2 min. The required memory space is also greatly reduced.