Image bilevel thresholding based on multiscale gradient multiplication

This paper introduces a novel global thresholding approach that exploits the multiscale gradient information. The multiscale gradient information, that is, the product of gradient magnitude (PGM), is obtained by multiplying the responses of the first derivative of Gaussian (FDoG) filter at three adjacent space scales. The output threshold is selected as the one that maximizes a new objective function of the gray level variable tt. The objective function is defined as the ratio of the mean PGM values of the boundary and non-boundary regions in the binary image obtained by thresholding with variable tt. Through analysis of 35 real images from different application areas, our results show that the proposed method can perform bilevel thresholding on the images with different histogram patterns, such as unimodal, bimodal, multimodal, or comb-like shape. Its segmentation quality is superior to five popular thresholding algorithms.

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► Present a multiscale gradient multiplication based thresholding method (MGMT).
► MGMT considers object position described by product of gradient magnitude.
► MGMT can perform bilevel thresholding on the images with different histogram patterns.
► Segmentation quality of MGMT is superior to five popular thresholding algorithms.