A Novel Algorithm for Image Thresholding Using Non-Parametric Fisher Information

The Fisher information (FI) measure is an important concept in statistical estimation theory and information theory. However, it has received relatively little consideration in image processing. In this paper, a novel algorithm is developed based on the nonparametric FI measure. The proposed algorithm determines the optimal threshold based on the FI measure by maximizing the measure of the separability of the resultant classes over all of the gray levels. The algorithm is compared with several classic thresholding methods on a variety of images, including some nondestructive testing (NDT) images and text document images. The experimental results show the effectiveness of the new method. Keywords: Image thresholding; Histogram; Fisher information; Information theory 1. Introduction The segmentation of images into homogeneous regions is an important area of research in computer vision. Image thresholding, which is a popular technique for image segmentation, is also regarded as an analytic image representation method [1]. This technique plays an important role in many of the tasks that are required for pattern recognition, computer vision, and video retrieval [2]. Image thresholding is computationally simpler than other existing algorithms, such as boundary detection or region dependent techniques [3-6]. Its aim is to find an appropriate threshold for separating the object of interest from the background. The output of a thresholding process is a binary image in which all of the pixels with gray levels higher than the determined threshold are classified as object and the remaining of pixels are assigned to background, or vice versa. This technique can be