基于局部熵的量子衍生医学超声图像去斑

摘要

针对现有医学超声图像去斑方法的不足，该文提出一种基于局部熵的量子衍生医学超声图像去斑新方法。首先，将对数变换后的图像进行双树复小波变换(DTCWT)，并对信号与噪声分别建模；然后，提取复小波中子代与父代小波系数的实部，计算其局部熵并进行归一化乘积，结合量子衍生理论得到用来调整信号与噪声出现概率的可调参数；最后，利用改进的双变量收缩函数获得去斑后的图像。通过实验，结果表明该方法与已有方法相比能够更有效地滤除医学超声图像中的斑点噪声并保留细节信息。%Aiming at the limitation of existing methods for the medical ultrasound images despeckling, a novel quantum-inspired despeckling method based on the local entropy is proposed for the medical ultrasound images. Firstly, the log-transformed images are decomposed by the Dual-tree Complex Wavelet Transform (DTCWT), and the signal and speckle noise are modeled separately. Then, considering the normalized products of the local entropy of the real components extracted from coefficients and their parents, the adjustable parameter is obtained by the quantum inspired theory to adjust the probability of signal and noise. Finally, the modified bivariate shrinkage function is exploited to obtain the despeckled image. The experimental results show that the proposed method can preserve detail information effectively and reduce the speckle noise of medical ultrasound image at the same time.