基于Renyi熵滤波的光声图像重建算法设计与实现

发布时间：2018-06-09 02:50

本文选题：光声图像重建 + 滤波处理算法　；参考：《生物化学与生物物理进展》2017年11期

【摘要】：针对光声图像重建过程中存在的原始光声信号信噪比差、重建图像对比度低、分辨率不足等问题,提出了基于Renyi熵的光声图像重建滤波算法.该算法首先根据原始光声信号的Renyi熵分布情况,确定分割阈值,并滤除杂波信号;再利用滤波后的光声数据进行延时叠加光声图像重建.利用该滤波算法分别处理铅笔芯横截面(零维)、头发丝(一维)以及小鼠大脑皮层血管(二维)等不同维度样本的光声信号,实验结果表明:相比Renyi熵处理之前,重建图像对比度平均增强了32.45%,分辨率平均提高了30.78%,信噪比提高了47.66%,均方误差降低了35.01%;相比典型的滤波处理算法(模极大值法和阈值去噪法),本研究中图像的对比度、分辨率和信噪比分别提高了25.94%/10.60%、27.90%/19.48%、35.21%/10.60%,均方误差减小了28.57%/16.66%.因此,选择利用Renyi熵滤波算法处理光声信号,从而使光声图像重建质量得到大幅改善.
[Abstract]:Aiming at the problems of poor signal-to-noise ratio (SNR) of original photoacoustic signal, low contrast of reconstructed image and insufficient resolution in the process of photoacoustic image reconstruction, a filter algorithm for photoacoustic image reconstruction based on Renyi entropy is proposed. Based on the Renyi entropy distribution of the original photoacoustic signal, the segmentation threshold is determined and the clutter signal is filtered. Then the filtered photoacoustic data are used to reconstruct the delayed superimposed photoacoustic image. The filter algorithm is used to process the photoacoustic signals of the lead pen core cross-section (zero dimension), hair filament (one dimension) and mouse cerebral cortical vessel (2 D) respectively. The experimental results show that: compared with Renyi entropy processing, The contrast of reconstructed image is increased by 32.45, the resolution is increased by 30.78, the SNR is increased by 47.66, and the mean square error is reduced by 35.01.Compared with the typical filtering algorithms (modulus maximum method and threshold denoising method), the contrast of the image in this study, The resolution and the signal-to-noise ratio were increased 25.94 / 10.60 and 27.90 / 19.48, 35.21 / 10.60, respectively, and the mean square error was reduced by 28.57% 16.66. Therefore, Renyi entropy filter algorithm is used to process photoacoustic signal, which can improve the quality of photoacoustic image reconstruction greatly.
【作者单位】：太原理工大学信息工程学院微纳系统研究中心教育部先进传感器与智能控制系统重点实验室;Department
【基金】：国家自然科学基金(61474079,11602159) 山西省优秀人才科技创新项目(201605D211020) 山西省高等学校科技创新项目(2016136)资助~~
【分类号】：TP391.41

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