Matrix Methods for Population Analysis

Structured-Population Models in Marine, Terrestrial, and Freshwater Systems ◽

10.1007/978-1-4615-5973-3_2 ◽

1997 ◽

pp. 19-58 ◽

Cited By ~ 19

Author(s):

Hal Caswell

Keyword(s):

Population Analysis ◽

Matrix Methods

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Matrix Methods of Population Analysis

Journal of the American Institute of Planners ◽

10.1080/01944366608978490 ◽

1966 ◽

Vol 32 (1) ◽

pp. 40-44 ◽

Cited By ~ 27

Author(s):

Andrei Rogers

Keyword(s):

Population Analysis ◽

Matrix Methods

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Automated 3-D cell population analysis in thick tissue sections using laser-scanning confocal-microscopy data

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100148642 ◽

1993 ◽

Vol 51 ◽

pp. 562-563

Author(s):

Hakan Ancin

Keyword(s):

Laser Scanning ◽

Population Analysis ◽

Three Dimensional ◽

Large Data ◽

Laser Scanning Confocal Microscopy ◽

Tissue Slices ◽

Scanning Confocal Microscopy ◽

Tissue Sections ◽

Spatially Adaptive ◽

Microscopy Data

This paper presents methods for performing detailed quantitative automated three dimensional (3-D) analysis of cell populations in thick tissue sections while preserving the relative 3-D locations of cells. Specifically, the method disambiguates overlapping clusters of cells, and accurately measures the volume, 3-D location, and shape parameters for each cell. Finally, the entire population of cells is analyzed to detect patterns and groupings with respect to various combinations of cell properties. All of the above is accomplished with zero subjective bias.In this method, a laser-scanning confocal light microscope (LSCM) is used to collect optical sections through the entire thickness (100 - 500μm) of fluorescently-labelled tissue slices. The acquired stack of optical slices is first subjected to axial deblurring using the expectation maximization (EM) algorithm. The resulting isotropic 3-D image is segmented using a spatially-adaptive Poisson based image segmentation algorithm with region-dependent smoothing parameters. Extracting the voxels that were labelled as "foreground" into an active voxel data structure results in a large data reduction.

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