&lt;title&gt;Unsupervised noise removal algorithms for 3-D confocal fluorescence microscopy&lt;/title&gt;

Roysam, Badrinath; Bhattacharjya, Anoop K.; Srinivas, Chukka; Szarowski, Donald H.; Turner, James N.

doi:10.1117/12.59558

Cited by 9 publications

(13 citation statements)

References 12 publications

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“…The Gaussian profiles are integrated over each bin. Each of these numbers lies between 0 and 1, and collectively they sum to 1, i.e., thus forming a unit simplex (Roysam and Bhattacharjya, 1992). The regression problem of interest is to determine the optimal number of Gaussians, M, the mixing fractions, w j , and the corresponding means, µ j, and variances, 2 j , for each of the Gaussians, such that the resulting equation represents the best possible fit to the histogram data, while also being as concise as possible (Rissanen, 1983).…”

Section: Resultsmentioning

confidence: 99%

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Application and Quantitative Validation of Computer-Automated Three-Dimensional Counting of Cell Nuclei

et al. 1999

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: 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.

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Section: Resultsmentioning

confidence: 99%

“…Automated computer analysis on the workstation used our previously reported algorithms operating on the 3-D image data Mackin et al, 1993Mackin et al, , 1998Roysam et al, 1992Roysam et al, , 1994Turner et al, 1997). The program adaptively segmented all the optical sections into background and foreground regions.…”

Section: Discussionmentioning

confidence: 99%

Application and Quantitative Validation of Computer-Automated Three-Dimensional Counting of Cell Nuclei

et al. 1999

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“…Since then, others have developed variations and improvements [72 -74]. specified by the user and many have subtle and/or nonlinear effects [53,54]. This is particularly challenging when the image analysis system must be configured for different classes of im-C. Adaptive 3D Image Segmentation.…”

Section: Methods For Handling the Storage And Computationalmentioning

confidence: 99%

“…Techniques for coping with such variability are especially important when the Our approach to deblurring is model based, and uses constrained maximum-likelihood estimation [38]. We have extended image analysis system is intended for unsupervised operation over large collections of samples [53,54]. The image models must this approach to perform a ''blind'' deconvolution [39], i.e., the PSF does not have to be measured or otherwise determined prior be able to adapt within the 3D image data set as well as between images.…”

Section: Methods For Handling Noise and Biologicalmentioning

confidence: 99%

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Automated image analysis technologies for biological 3D light microscopy

Turner

Ancin

Becker

et al. 1997

Int. J. Imaging Syst. Technol.

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Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei

et al. 2004

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Background: Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous regionbased methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed. Methods: A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree.

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<title>Unsupervised noise removal algorithms for 3-D confocal fluorescence microscopy</title>

Cited by 9 publications

References 12 publications

Application and Quantitative Validation of Computer-Automated Three-Dimensional Counting of Cell Nuclei

Application and Quantitative Validation of Computer-Automated Three-Dimensional Counting of Cell Nuclei

Automated image analysis technologies for biological 3D light microscopy

Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei

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