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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.

This paper presents a landmark based method for efficient, robust, and automated computational synthesis of high‐resolution, two‐dimensional (2‐D) or three‐dimensional (3‐D) wide‐area images of a specimen from a series of overlapping partial views. The synthesized image is the set union of the areas or volumes covered by the partial views, is called the “montage.” This technique is used not only to produce gray‐level montages, but also to montage the results of automated image analysis, such as 3‐D cell segmentation and counting, so as to generate large representations that are equivalent to processing the large wide‐area image at high resolution. The method is based on computing a concise set of feature‐tagged landmarks in each partial view, and establishing correspondences between the landmarks using a combinatorial point matching algorithm. This algorithm yields a spatial transformation linking the partial views that can be used to create the montage. Such processing can be a first step towards high‐resolution large‐scale quantitative tissue studies. A detailed example using 3‐D laser‐scanning confocal microscope images of acriflavine‐stained hippocampal sections of rat brain is presented to illustrate the method. © 1996 Wiley‐Liss, Inc.

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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.