Brownian strings: image segmentation with stochastically deformable models

R. Grzeszczuk, D. Levin
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引用次数: 2

Abstract

This paper describes an image segmentation technique in which an arbitrarily shaped contour is deformed stochastically until it fits around an object of interest. The evolution of the contour is controlled by a simulated annealing process which causes the contour to settle into the global minimum of an image-derived 'energy' function which is designed to be small when the contour is near the border of objects similar to the target. The nonparametric energy function is derived from the statistical properties of similar previously segmented images, thereby incorporating prior experience. Since the method is based on a state space search for the contour with the best global properties, it is stable in the presence of image errors which confound segmentation techniques based on local criteria such as connectivity. However, unlike 'snakes' and other active contour approaches, the new method can handle arbitrarily irregular contours in which each inter-pixel crack represents an independent degree of freedom. The method is illustrated by using it to find the brain surface in magnetic resonance head images, to identify the epicardial surface in magnetic resonance cardiac images, and to track blood vessels in angiograms.
布朗串:随机变形模型的图像分割
本文描述了一种图像分割技术,其中任意形状的轮廓随机变形,直到它适合感兴趣的对象周围。轮廓的演化由模拟退火过程控制,该过程使轮廓沉降到图像派生的“能量”函数的全局最小值,当轮廓靠近与目标相似的物体的边界时,该函数被设计为较小。非参数能量函数来源于类似的先前分割图像的统计特性,从而结合了先前的经验。由于该方法是基于对具有最佳全局属性的轮廓的状态空间搜索,因此它在存在图像误差的情况下是稳定的,这些误差会混淆基于连通性等局部标准的分割技术。然而,与“蛇”和其他主动轮廓方法不同,新方法可以处理任意不规则轮廓,其中每个像素间裂缝代表一个独立的自由度。应用该方法在磁共振头部图像中寻找脑表面,在磁共振心脏图像中识别心外膜表面,在血管成像中跟踪血管。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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