State GRID Quzhou Power Supply Company, No.6, Xinhe Road, Quzhou, Zhejiang 324000, China.
College of Electrical and Information Engineering, Quzhou University, Quzhou, Zhejiang 324000, China.
PLoS One. 2020 Mar 3;15(3):e0229651. doi: 10.1371/journal.pone.0229651. eCollection 2020.
Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image's brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a new thresholding method is proposed in this paper. The proposed method constructs a new two dimensional histogram using brightness of a pixel and local relative entropy of it's neighbor pixels. The local relative entropy (LRE) measures the brightness difference between a pixel and it's neighbor pixels. The two dimensional histogram, consisting of gray level and LRE, can reflect the contextual information between pixels to a certain extent. The optimal thresholding vector is obtained via minimizing cross entropy criteria. Experimental results show that the proposed method can achieve more accurate segmentation results than other thresholding methods.
虽然传统的阈值方法简单高效，但由于在阈值选择过程中只考虑了图像的亮度信息，因此可能会导致较差的分割结果。考虑像素之间的上下文信息可以提高分割精度。为此，本文提出了一种新的阈值方法。该方法使用像素的亮度和其邻域像素的局部相对熵构建一个新的二维直方图。局部相对熵（LRE）度量像素与其邻域像素之间的亮度差异。二维直方图由灰度和 LRE 组成，可以在一定程度上反映像素之间的上下文信息。通过最小化交叉熵准则来获得最佳的阈值向量。实验结果表明，与其他阈值方法相比，该方法可以获得更准确的分割结果。
