基于累积剩余信息能量的图像阈值分割法

Image Threshold Segmentation Method Based on Cumulative Residual Information Energy

Shannon定义的信息熵是一种度量信息不确定性的方式,Onicescu定义的信息能量是一种度量信息确定性的方式,信息能量与Shannon熵呈现一种对偶关系。累积剩余熵是使用累积分布函数替换Shannon熵的概率分布函数来度量信息不确定性的方式,基于此,提出一种新的度量信息确定性的方式——累积剩余信息能量,并将累积剩余信息能量应用于图像的阈值分割。为了克服累积剩余信息能量自身存在的计算复杂、效率低的缺点,使用递归算法提升图像阈值分割的运行速度。与经典的最大熵阈值法等相关阈值分割法的对比实验结果表明,所提方法对于自然图像与细胞血涂片图像分割均具有一定的优越性。

Generally,Shannon defined information entropy is used to measure information uncertainty,whereas,Onicescu defined information energy is used to measure information certainty.However,information energy and Shannon entropy display a dual relationship.Furthermore,the cumulative residual entropy is employed to estimate the information uncertainty by replacing the probability distribution function of Shannon entropy with the cumulative distribution function.Based on this,a new method to measure information certainty—cumulative residual information energy—is proposed and applied to image threshold segmentation.To overcome the shortcomings of complex calculation and the low efficiency of accumulated residual information energy,a recursive algorithm is used here to increase the running speed of image threshold segmentation.Our experimental results show that the proposed method outperforms the classical maximum entropy threshold method and other related threshold segmentation methods used for natural images and cell blood smear images.