ON THE PRECISION OF THE ISOTROPIC CAVALIERI DESIGN

IF 0.8 4区 计算机科学 Q4 IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY Image Analysis & Stereology Pub Date : 2018-12-06 DOI:10.5566/IAS.1947
Javier González-Villa, M. Cruz, L. Cruz-Orive
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Abstract

The isotropic Cavalieri design is based on a isotropically oriented set of parallel systematic sections a constant distance apart. Its advantage over the ordinary Cavalieri design is twofold - first, besides volume it allows the unbiased estimation of surface area, and second, the error variance predictor for the volume estimator is much simpler, involving only the surface area of the object, and the distance between sections. In an earlier paper, the two hemispheres of a rat brain were arranged perpendicular to each other before sectioning, aiming at reducing the error variance with respect to other arrangements (such as the aligned one) by exploiting an intuitively plausible antithetic effect. Because the total surface area of the objects is unchanged under any arrangements, however, the error variance predictor for the volume estimator does not depend on object shape, which looks intriguing. Using reconstructions of the mentioned hemispheres, we dilucidate the aparent paradox by means of automatic Monte Carlo replications of the relevant volume estimates under the antithetic and the aligned arrangements.
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论各向同性卡瓦列里设计的精度
各向同性的卡瓦列里设计是基于一组各向同性的平行系统截面,它们之间的距离是恒定的。与普通的Cavalieri设计相比,它的优点是双重的——首先,除了体积之外,它允许对表面积进行无偏估计;其次,体积估计器的误差方差预测器要简单得多,只涉及物体的表面积和部分之间的距离。在早期的一篇论文中,在切片之前,将大鼠大脑的两个半球垂直排列,目的是通过利用直觉上合理的对偶效应,减少相对于其他排列(如对齐的那个)的误差方差。然而,由于物体的总表面积在任何安排下都是不变的,体积估计器的误差方差预测器不依赖于物体形状,这看起来很有趣。利用上述半球的重建,我们通过自动蒙特卡罗复制在对位和对齐排列下的相关体积估计来淡化明显的悖论。
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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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