多向加权 Tsallis 熵最大化导向的自动阈值分割方法

Affected by many different factors, such as noise or random details, the size ratio of target to background, or the point spread function during imaging, the gray level histograms of many images appear as non-modal, unimodal, bimodal or multimodal patterns. To deal with the issue of automatic threshold selection in these four different modal situations within a unified framework, an automatic thresholding segmentation method guided by maximizing the multi-directional weighted Tsallis entropy is proposed in this paper. Based on the multi-scale product effect of a newly designed arctangent directional convolution kernel, the proposed method first converts the gray level histograms of different modalities into a unified unimodal right-biased gray level histogram. After extracting this special unimodal right-biased gray level histogram in four different directions, a multi-directional weighted strategy is utilized to construct a weighted Tsallis entropy objective function that is closely related to the gray levels of the original image. When the objective function takes the maximum value, the corresponding gray level is used as the final segmentation threshold. The proposed method is compared with three thresholding methods, one soft segmentation method, one active contour method and one automatic clustering segmentation method. The experimental results on four synthetic images and fifty real-world images in four different modal situations show that although the proposed method has no advantage in terms of computational efficiency, it has more robust segmentation adaptability to test images of different modalities, and it is superior to the other six segmentation methods in terms of Matthews correlation coefficient used to quantify segmentation accuracy.