Fast thresholding based on improved minimum cross entropy

Image segmentation is an important and classic problem in image processing and computer vision. Thresholding is applied to many fields, because of its less computation and stable performance. The key of thresholding method is to determine the adaptive threshold. In order to segment biological image effectively, a new adaptive thresholding method is proposed. First, two dimension minimum entropy is computed based on gray-gradient co-occurrence matrix; and then the genetic algorithm is applied to encode the two-dimension threshold vector; Finally, the optimum threshold is calculated based on fitness function and uniformity measurement(UM). Experimental results show that this method has three advantages: 1) improve computational efficiency so that it can run in real time; 2) retain more object and edge information so that it can meet the practical requirement; 3) robust to the uneven distribution of light.