Algorithms for automated characterization of cell populations in thick specimens from 3-D confocal fluorescence microscopy data.

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.

Joint solution of low, intermediate, and high-level vision tasks by evolutionary optimization: Application to computer vision at low SNR.

Methods for conducting model-based computer vision from low-SNR (=/<1 dB) image data are presented. Conventional algorithms break down in this regime due to a cascading of noise artifacts, and inconsistencies arising from the lack of optimal interaction between high- and low-level processing. These problems are addressed by solving low-level problems such as intensity estimation, segmentation, and boundary estimation jointly (synergistically) with intermediate-level problems such as the estimation of position, magnification, and orientation, and high-level problems such as object identification and scene interpretation. This is achieved by formulating a single objective function that incorporates all the data and object models, and a hierarchy of constraints in a Bayesian framework. All image-processing operations, including those that exploit the low and high-level variables to satisfy multi-level pattern constraints, result directly from a parallel multi-trajectory global optimization algorithm. Experiments with simulated low-count (7-9 photons/pixel) 2-D Poisson images demonstrate that compared to non-joint methods, a joint solution not only results in more reliable scene interpretation, but also a superior estimation of low-level imaging variables. Typically, most object parameters are estimated to within a 5% accuracy even with overlap and partial occlusion.

Hierarchically structured unit-simplex transformations for parallel distributed optimization problems.

A stable deterministic approach is presented for incorporating unit-simplex constraints based on a hierarchical deformable-template structure. This approach (i) guarantees strict confinement of the search to the unit-simplex constraint set without introducing unwanted constraints; (ii) leads to a hierarchical, rather than a global, network interconnection structure; (iii) allows multiresolution processing; and (iv) allows easy closed-form incorporation of certain other inherently global constraints, such as general recursive symmetries. Selected examples are presented which illustrate and demonstrate large-scale application of the template method.