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Threshold Selection using Partial Structural Similarity

A novel image threshold selection approach based on structural similarity (SSIM) is proposed. The thresholded image is obtained first, then comparison regions are extracted based on the local variance of the neighborhood of the thresholded image. Due to the characteristic of comparison regions, the conventional SSIM expression is simplified as a nonparametric form, and the partial SSIM (PSSIM) is defined. The optimal threshold is selected by maximizing the PSSIM criterion function at last. Besides the introduction of a novel approach, this is also the first attempt to expand the application scope of SSIM to range image thresholding in general. The proposed approach has an advantage over thresholding methods based on the histogram. The method was tested on a variety of images including the synthetic image and real images. Experimental results show that the proposed approach achieves better applicability, preferable ability for extracting object and better anti-noise capability than popular methods.