Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Nuclear texture measurements in image cytometry.
151
Zitationen
4
Autoren
1995
Jahr
Abstract
DNA image cytometry is widely used in cytopathology as a means to obtain objective information concerning the diagnosis and prognosis of human cancer. Using specially designed devices, the high resolution spatial and photometric information is available in the images of a microscopic field. If quantitative DNA specific stains are used the chromatin distribution in the cell nuclei can be measured, which is one of the critical features for cytopathological analysis. In normal cells, changes in the chromatin appearance reflect changes in the activation patterns of genes. In tumors, dramatic changes in the nuclear chromatin appearance are common and have been associated with the progression of the disease. Features describing the chromatin distribution pattern are referred to as texture features. Nuclear texture features are sensitive to the differences between the various descriptive classes of chromatin patterns. In this paper we discuss the main categories of nuclear texture measurements. Texture features can be roughly divided into the following categories: 1) descriptive statistics of chromatin distribution; 2) discrete texture features; 3) range extreme; 4) markovian; 5) run length and 6) fractal texture features. Representative features of each of the above categories are discussed together with mathematical formulas, simple figures for explanation as well as images of typical cells which differ significantly in some texture features. Key references are also provided.
Ähnliche Arbeiten
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2−ΔΔCT Method
2001 · 180.080 Zit.
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
2005 · 56.074 Zit.
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
2009 · 44.149 Zit.
limma powers differential expression analyses for RNA-sequencing and microarray studies
2015 · 42.438 Zit.
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
2012 · 37.624 Zit.