A Novel Image Thresholding Method Combining Entropy With Parzen Window Estimation

Abstract Image thresholding is an important and efficient image segmentation technique, which is crucial and essential for image analysis and computer vision. In this paper, we proposed a new image thresholding method based on entropy and parzen window (PW) estimation. First, the probability of each gray-level distribution is approximate by using the PW estimation. Second, by combining the obtained probability information with entropic information of the foreground and background, a new objective function is created. At last, the ideal threshold value is obtained by optimizing the objective function. By comparing with some classical thresholding methods, such as inter class variance method (OTSU), minimum error thresholding method (MET), Kapur’s entropy based method (KAPUR) and the recent methods that take spatial information into consideration (2D-D histogram method, GLLV histogram method and Gabor histogram method), the proposed method, experiment on 10 images (one synthetic image, four nondestructive testing images and five real-world images), presents a better performance on the accuracy, robustness and visual effect of segmentation.