Computer analysis of two‐dimensional gels by a general image processing system

Tyson, John J.; Haralick, R. H.

doi:10.1002/elps.1150070302

Cited by 21 publications

(8 citation statements)

References 17 publications

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“…Gaussian approximations to protein spots are commonly used in 2-DE [16][17][18]. The idea of using the Gaussian fit as a parameter is based on the assumption that all single, isolated protein spots should ideally display the shape of a perfect Gaussian peak.…”

Section: Gaussian Fitmentioning

confidence: 99%

A multivariate spot filtering model for two‐dimensional gel electrophoresis

Rye

Alsberg

2008

Electrophoresis

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Image segmentation plays an important role in the automatic analysis of protein spots in 2-DE. Using image segments representing protein spots, the amount of protein in each segment can be quantified, and corresponding segments can be matched and compared for multiple gels. However, the common presence of image segments caused by noise and unwanted artefacts highly disturb the analysis and comparison of the gels. Common sources of such artefacts are cracks in the gel surface, fingerprints, dust and other pollutions. It would be advantageous to remove these unwanted artefacts during or after the segmentation procedure. To achieve this task a multivariate spot filtering model is developed using image segments from a gel segmentation. Parameters in the model are based on texture, shape and intensity measurements of the image segments. The model successfully managed to separate segments caused by noise, artefacts and cracks from image segments representing true protein spots. The classification method used is discriminant partial least squares regression.

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Section: Gaussian Fitmentioning

confidence: 99%

A multivariate spot filtering model for two‐dimensional gel electrophoresis

Rye

Alsberg

2008

Electrophoresis

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“…A simple approach is to obtain the lightest and darkest point in the background and replace the whole background with the average intensity. Tyson and Haralick [47] find the local minima in the image, representing background depressions, and interpolate the background between these minima. Melanie II [34] subtracts the minimum [46].…”

Section: Intensity Correctionmentioning

confidence: 99%

The role of bioinformatics in two‐dimensional gel electrophoresis

2003

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Over the last two decades, two-dimensional electrophoresis (2-DE) gel has established itself as the de facto approach to separating proteins from cell and tissue samples. Due to the sheer volume of data and its experimental geometric and expression uncertainties, quantitative analysis of these data with image processing and modelling has become an actively pursued research topic. The results of these analyses include accurate protein quantification, isoelectric point and relative molecular mass estimation, and the detection of differential expression between samples run on different gels. Systematic errors such as current leakage and regional expression inhomogeneities are corrected for, followed by each protein spot in the gel being segmented and modelled for quantification. To assess differential expression of protein spots in different samples run on a series of two-dimensional gels, a number of image registration techniques for correcting geometric distortion have been proposed. This paper provides a comprehensive review of the computation techniques used in the analysis of 2-DE gels, together with a discussion of current and future trends in large scale analysis. We examine the pitfalls of existing techniques and highlight some of the key areas that need to be developed in the coming years, especially those related to statistical approaches based on multiple gel runs and image mining techniques through the use of parallel processing based on cluster computing and the grid technology.

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“…Several methods have been suggested to tackle with 2D-GE image analysis such as stepwise thresholding [4], edge detection [5] and watersheds [6]. Stepwise thresholding applies an increasing threshold on the 2D-GE image of interest, starting from the lowest intensity level which can be associated with a protein spot.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised 2D gel electrophoresis image segmentation based on active contours

Savelonas

Mylona

Maroulis

2012

Pattern Recognition

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This work introduces a novel active contour-based scheme for unsupervised segmentation of protein spots in two-dimensional gel electrophoresis (2D-GE) images. The proposed segmentation scheme is the first to exploit the attractive properties of the active contour formulation in order to cope with crucial issues in 2D-GE image analysis, including the presence of noise, streaks, multiplets and faint spots. In addition, it is unsupervised, providing an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. It is based on the formation of a spottargeted level-set surface, as well as of morphologically-derived active contour energy terms, used to guide active contour initialization and evolution, respectively. The experimental results on real and synthetic 2D-GE images demonstrate that the proposed scheme results in more plausible spot boundaries and outperforms all commercial software packages in terms of segmentation quality.

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Computer analysis of two‐dimensional gels by a general image processing system

Cited by 21 publications

References 17 publications

A multivariate spot filtering model for two‐dimensional gel electrophoresis

A multivariate spot filtering model for two‐dimensional gel electrophoresis

The role of bioinformatics in two‐dimensional gel electrophoresis

Unsupervised 2D gel electrophoresis image segmentation based on active contours

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