Ultrasonic image speckles result from the interference of the reflected signals by the scatters in the detected tissue. The physical characteristics of the speckles are closely correlated with the structures of the biological tissues, and the probability distribution of these speckles differs across different tissues. Based on the probability characteristics of intravascular ultrasound (IVUS) speckles, a Gamma mixture model and Gaussian mixture model are proposed to describe the calcified plaque, soft plaque and normal vascular regions on IVUS images. Using KS test, KL divergence and correlation coefficient analysis, we found that the probability distributions of the speckles generated by calcified plaques and normal blood vessels were better described by the Gaussian mixture model, while the speckles caused by soft plaques were described better by the Gamma mixture model. Based on this finding, we propose a probability mixture model combining neighborhood information for plaque segmentation on IVUS images. Compared with the existing probabilistic mixture model, the segmentation accuracy was greatly improved with a reduced noise.%超声斑点是由人体组织中散射体的反射信号相干作用所形成的,其概率分布与生物组织的结构信息密切相关,即不同的组织结构所产生斑点的概率分布形式不同.根据血管内超声(IVUS)斑点的概率分布特性,本文提出用斑点的伽马混合模型和高斯混合模型分别描述血管内超声组织中的钙化斑块、软斑块和正常血管区域.通过KS检验,KL散度和相关系数等指标分析,发现钙化斑块和正常血管区域的斑点概率分布符合高斯混合模型,而软斑块更接近伽马混合模型.在此研究基础上,本文提出一种结合邻域信息的概率混合模型,用于IVUS图像斑块分割,与现有的概率混合模型比较,分割精度大大地提高,且受噪声影响减少.
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