重新讨论不等截面间距的卡瓦列里估计量

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY Image Analysis & Stereology Pub Date : 2017-06-23 DOI:10.5566/IAS.1723
M. Kiderlen, K. Dorph‐Petersen
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引用次数: 2

摘要

卡瓦列里方法允许通过等距平行平面剖面的面积测量来估计紧凑物体的体积。然而,截面的间距和厚度在应用中可能是相当不规则的。因此,最近的出版物集中在截面间距随机变异性的影响上,表明当平行平面的堆栈是平稳的时,经典的Cavalieri估计仍然是无偏的,但现有的方差近似必须进行调整。本文考虑了一种特殊情况,在这种情况下,连续截面平面之间的距离可以测量,因此卡瓦列里估计量可以用随机抽样点的正交规则代替。我们证明,在温和的条件下,梯形规则和辛普森规则导致无偏体积估计,并给出仿真结果表明,与广义Cavalieri估计相比,可以实现相当大的方差减小。
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THE CAVALIERI ESTIMATOR WITH UNEQUAL SECTION SPACING REVISITED
The Cavalieri method allows to estimate the volume of a compact object from area measurements in equidistant parallel planar sections. However, the spacing and thickness of sections can be quite irregular in applications. Recent publications have thus focused on the effect of random variability in section spacing, showing that the classical Cavalieri estimator is still unbiased when the stack of parallel planes is stationary, but that the existing variance approximations must be adjusted. The present paper considers the special situation, where the distances between consecutive section planes can be measured and thus where Cavalieri’s estimator can be replaced by a quadrature rule with randomized sampling points. We show that, under mild conditions, the trapezoid rule and Simpson’s rule lead to unbiased volume estimators and give simulation results that indicate that a considerable variance reduction compared to the generalized Cavalieri estimator can be achieved.
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来源期刊
Image Analysis & Stereology
Image Analysis & Stereology MATERIALS SCIENCE, MULTIDISCIPLINARY-MATHEMATICS, APPLIED
CiteScore
2.00
自引率
0.00%
发文量
7
审稿时长
>12 weeks
期刊介绍: Image Analysis and Stereology is the official journal of the International Society for Stereology & Image Analysis. It promotes the exchange of scientific, technical, organizational and other information on the quantitative analysis of data having a geometrical structure, including stereology, differential geometry, image analysis, image processing, mathematical morphology, stochastic geometry, statistics, pattern recognition, and related topics. The fields of application are not restricted and range from biomedicine, materials sciences and physics to geology and geography.
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