次方样本熵自适应加权的超声合成孔径成像算法

发布时间：2018-04-02 10:21

  本文选题：合成孔径　切入点：成像算法　出处：《声学学报》2017年01期

【摘要】：利用相位相干系数(PCF)和广义相干系数(GCF)对波束形成后的结果进行加权,能有效提高超声成像的质量,但存在背景组织亮度降低,对比度不高,以及远处目标成像强度降低等问题。本文提出一种基于次方样本熵的合成孔径成像算法,将单个孔径发射时的低质量成像结果作为元素,根据孔径位置排列,构成空间向量。根据不同成像点对应的空间向量的随机性不同,计算每个点的空间向量的次方样本熵,并将该熵值作为权系数进行加权成像。采用FieldⅡ仿真数据成像结果表明,相比于传统的DAS算法,次方样本熵方法能够提高成像的分辨率和对比度;相比于PCF和GCF算法,次方样本熵方法能够在不损失组织背景强度的情况下,进一步改善了成像质量。
[Abstract]:The results of beamforming can be weighted by using phase coherence coefficient PCF (PCF) and generalized coherence coefficient (GCF), which can effectively improve the quality of ultrasonic imaging, but the brightness of background tissue is low and the contrast is not high. In this paper, a synthetic aperture imaging algorithm based on power sample entropy is proposed, in which the low quality imaging results of a single aperture are taken as elements and arranged according to the position of aperture. According to the randomness of the space vectors corresponding to different imaging points, the entropy of the space vectors of each point is calculated, and the entropy value is used as the weight coefficient for weighted imaging. The results of Field 鈪，

本文编号：1699942