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核染色质的自动纹理分析。一种数学形态学方法。

Automated textural analysis of nuclear chromatin. A mathematical morphology approach.

作者信息

Giménez-Mas J A, Sanz-Moncasi M P, Remón L, Gambó P, Gallego-Calvo M P

机构信息

Department of Pathology, Hospital Miguel Servet, Zaragoza, Spain.

出版信息

Anal Quant Cytol Histol. 1995 Feb;17(1):39-47.

PMID:7766267
Abstract

Nuclear grading of neoplasms has classically been involved in prognosis and must be established by combining different parameters, such as the textural pattern of chromatin, which is subjective and difficult to measure. Mathematical morphology (MM), a branch of mathematics dealing with shapes, and, in particular, the so-called top hat transformation, provides us with a helpful tool for quantitative assessment of chromatin texture. A sequence of MM operations (the top-hat transformation) was applied to Mayer-hematoxylin-stained cytologic smears made immediately after surgical removal to obtain a series of images at different levels of a granulometric chromatin fractionation. These images are related to the size (n = 1, 2, 4, 6 and 8) of a structuring element that performs these operations. A skeletonization of the intergranular area at level 4 was also performed to provide a shape-related image of chromatin grains. Using these granulometric images as a starting point, we defined a series of variables: TH(n) as the granulometric area at top-hat level n; GAD(n) as the grain-associated density at level n; THIOD(n) as the integrated optical density of the granular fraction at level(n); GIOD(n) as the grain-integrated optical density at level n; CP as a chromatin texture variable, chromatin pattern, that estimates the granular versus dispersed aggregation pattern; and CB, a shape descriptor that estimates the roughness of the isolated chromatin grains and is expressed as a coefficient related to the number of branches of the intergranular skeleton. The operation provides a set of variables descriptive of a wide range of chromatin texture properties.(ABSTRACT TRUNCATED AT 250 WORDS)

摘要

肿瘤的核分级传统上与预后相关,必须通过综合不同参数来确定,比如染色质的纹理模式,而这具有主观性且难以测量。数学形态学(MM)是一门处理形状的数学分支,特别是所谓的顶帽变换,为我们提供了一个定量评估染色质纹理的有用工具。一系列MM操作(顶帽变换)应用于手术切除后立即制作的美蓝苏木精染色的细胞学涂片,以获得一系列不同粒度染色质分级水平的图像。这些图像与执行这些操作的结构元素的大小(n = 1、2、4、6和8)有关。还对第4级的颗粒间区域进行了骨架化处理,以提供与染色质颗粒形状相关的图像。以这些粒度图像为起点,我们定义了一系列变量:TH(n)为顶帽水平n处的粒度面积;GAD(n)为水平n处的颗粒相关密度;THIOD(n)为水平(n)处颗粒部分的积分光密度;GIOD(n)为水平n处的颗粒积分光密度;CP为染色质纹理变量,即染色质模式,用于估计颗粒状与分散聚集模式;以及CB,一个形状描述符,用于估计孤立染色质颗粒的粗糙度,并表示为与颗粒间骨架分支数量相关的系数。该操作提供了一组描述广泛染色质纹理特性的变量。(摘要截断于250字)

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