Image thresholding segmentation method based on minimum square rough entropy

Abstract

Image thresholding based on rough entropy is an efficient image segmentation technique. The optimal thresholds of the existing exponential and logarithmic rough entropy thresholding segmentation algorithms have unclear physical meaning. In this paper, a new form of square rough entropy is defined to measure the roughness in an image, and the corresponding image thresholding segmentation algorithm is proposed. The novel square rough entropy has good properties and simple computation. In the proposed thresholding algorithm, the optimal threshold is at the boundary between the object and the background of an image. This process is consistent with the expectation of image bi-level thresholding segmentation. To effectively granulate the image, a granule size selection method based on the homogeneity histogram is proposed, which is helpful in taking care of small objects and local variations of the images. The proposed thresholding method is evaluated by comparison with other three existing rough entropy based thresholding methods and three state-of-the-art image thresholding methods both qualitatively and quantitatively, using the natural images, the non-destructive testing images and an infrared video sequence from the OTCBVS Benchmark Data set. The comparison confirms the effectiveness of the proposed algorithm that not only is simple in form and clear in meaning, but also has good segmentation effect.