An improved method for image thresholding based on the valley-emphasis method

Thresholding is an important technique for image segmentation that extracts a target from its background on the basis of the distribution of gray levels. Many automatic threshold selection methods such as Otsu method provide satisfactory results for thresholding images with obvious bimodal gray level distribution. However, most threshold selection methods fail if the histogram is unimodal or close to unimodal. Valley-emphasis method partially resolves such problem by weighting the objective function of the Otsu method with the valley point in the histogram. In this study, we proposed an approach for improving the valley-emphasis method for optimal threshold selection by introducing a Gaussian weighting scheme to enhance the weighting effect. Experimental results indicate that the proposed method provides better and more stable thresholding results.