SUMMARY Methods are presented for the automated, quantitative and three‐dimensional (3‐D) analysis of cell populations in thick, essentially intact tissue sections while maintaining intercell spatial relationships. This analysis replaces current manual methods which are tedious and subjective. The thick sample is imaged in three dimensions using a confocal scanning laser microscope. The stack of optical slices is processed by a 3‐D segmentation algorithm that separates touching and overlapping structures using localization constraints. Adaptive data reduction is used to achieve computational efficiency. A hierarchical cluster analysis algorithm is used automatically to characterize the cell population by a variety of cell features. It allows automatic detection and characterization of patterns such as the 3‐D spatial clustering of cells, and the relative distributions of cells of various sizes. It also permits the detection of structures that are much smaller, larger, brighter, darker, or differently shaped than the rest of the population. The overall method is demonstrated for a set of rat brain tissue sections that were labelled for tyrosine hydroxylase using fluorescein‐conjugated antibodies. The automated system was verified by comparison with computer‐assisted manual counts from the same image fields.
In this paper, we present a scheme for embedding data in copies (color or monochrome) of predominantly text pages that may alsocontain color images or graphics. Embedding data imperceptibly in documents or images is a key ingredient of watermarking and data hiding schemes. It is comparatively easy to hide a signal in natural images since the human visual system is less sensitive to signals embedded in noisy image regions containing high spatial j-equencies. In other instances, e.g., simple graphics or monochrome text documents, additional constraints need to be satisfied to embed signals imperceptibly. Data may be embedded imperceptibly in printed text by altering some measurable property of a font such as position of a character or font size. This scheme however, is not very usefir1 for embedding data in copies of text pages, as that would require accurate text segmentation and possibly optical character recognition, both of which would deteriorate the error rate performance of the data-embedding system considerably. Similarly, other schemes that alter pixels on text boundaries have poor performance due to boundarydetection uncertainties introduced by scanner noise, sampling and blurring. The scheme presented in this paper ameliorates the above problems by using a textregion based embedding approach. Since the bulk of documents reproduced t o w contain black on white text, this data-embedding scheme can form a print-level layer in applications such as copy tracking and annotation.
: This study provides a quantitative validation of qualitative automated three-dimensional (3-D) analysis methods reported earlier. It demonstrates the applicability and quantitative accuracy of our method to detect, characterize, and count Feulgen stained cell nuclei in two tissues (hippocampus and testes). These methods can provide important insights into the interpretation of biological, pharmacological, pathological, and toxicological events. A laser-scanned confocal light microscope was used to record 3-D images in which our algorithms automatically identified individual nuclei from the optical sections given an estimate of minimum nuclear size. The hippocampal data sets were also manually counted independently by five trained observers using the STERECON 3-D image reconstruction system. The automated and manual counts were compared. The computer counts were lower ( approximately 14%) than the manual counts, mainly because the algorithms counted a nucleus only if it was present in five consecutive optical sections but the human counters included nuclei that were in fewer optical sections. A nucleus-by-nucleus comparison of the manual and automated counts verified that the automated analysis was accurate and reproducible, and permitted additional quantitative analyses not available from manual methods. The algorithms also identified subpopulations of nuclei within the hippocampal samples, and haploid and diploid nuclei in the testes. Our methods were shown to be repeatable, accurate, and more consistent than manual counting. Nuclei in regions of high (hippocampal pyramidal layer) and low (extrapyramidal layer) density were distinguished with equal ease. Haploid and diploid nuclei were distinguished in the testes, demonstrating that our automated method may be useful for ploidy analysis. The results presented here on hippocampus and testis are consistent with other qualitative results from the liver and from immunohistochemically labeled substantia nigra, demonstrating the applicability of our software across tissues and preparation methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.