Statistical methods for automatic segmentation of elastographic images

S. Nedevschi, C. Pantilie, T. Mariţa, S. Dudea
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引用次数: 5

Abstract

Elastography is a new ultrasonic method for measuring tissuespsila elasticity. Besides many and widely acknowledged benefits, the method suffers severe limitations due to the high motion sensitivity and inter-operator dependency to the point where it provides only qualitative information, not having, until now, any real quantification means. In this paper we present an automatic segmentation method for elastographic images based on statistical techniques. First a probabilistic model is built for every pixel in the image, derived by processing a video sequence instead of a single image. The built image contains the values with highest probability for each pixel. Next a DAEM (Deterministic Annealing Expectation Maximization) method is used for automatic image segmentation. Finally a numerical quantification of tissue elasticity is provided based on the segmentation.
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弹性体图像自动分割的统计方法
弹性成像是一种测量组织弹性的超声新方法。除了许多公认的优点外,由于运动灵敏度高和操作者之间的依赖关系,该方法存在严重的局限性,因此它只能提供定性信息,到目前为止还没有任何真正的量化手段。本文提出了一种基于统计技术的弹性图像自动分割方法。首先,通过处理视频序列而不是单个图像,为图像中的每个像素建立概率模型。构建的图像包含每个像素具有最高概率的值。然后采用确定性退火期望最大化(DAEM)方法对图像进行自动分割。最后给出了基于分割的组织弹性的数值量化方法。
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