A fast spot segmentation algorithm for two‐dimensional gel electrophoresis analysis

Wu, Yecheng; Lemkin, Peter F.; Upton, Kyle

doi:10.1002/elps.11501401208

Cited by 17 publications

(7 citation statements)

References 14 publications

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“…LoG's sensitivity to noise requires the image to be Gaussian prefiltered. The core areas are then propagated to neighboring pixels using heuristics based on the intensity values and second derivatives [50], or by fitting a 2-D polynomial to the core [31].…”

Section: Spot Detectionmentioning

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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Section: Spot Detectionmentioning

confidence: 99%

The role of bioinformatics in two‐dimensional gel electrophoresis

2003

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“…Such "multiplets" tend to occupy a large portion of the gel surface and their detection is often very difficult. Currently, biologists perform a laborious, errorprone process involving the correction of the output generated by 2-D PAGE image analysis software packages, such as Melanie (Geneva Bioinformatics SA, Geneva, Switzerland) [2]. The development of automated and consistent computer-based spot detection techniques is of crucial importance.…”

Section: Introductionmentioning

confidence: 99%

A Computer-Based Technique for Automated Spot Detection in Proteomics Images

Mylona

Savelonas

Maroulis

et al. 2011

IEEE Trans. Inform. Technol. Biomed.

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Abstract-This paper introduces a novel computer-based technique for automated detection of protein spots in proteomics images. The proposed technique is based on the localization of regional intensity maxima associated with protein spots and is formulated so as to ignore rectangular-shaped streaks, minimize the detection of false negatives, and allow the detection of multiple overlapping spots. Regional intensity constraints are imposed on the localized maxima in order to cope with the presence of noise and artifacts. The experimental evaluation of the proposed technique on real proteomics images demonstrates that it: 1) achieves a predictive value (PV) and detection sensitivity (DS) which exceed 90%; 2) outperforms Melanie software package in terms of PV, specificity, and DS; 3) ignores artifacts; 4) distinguishes multiple overlapping spots; 5) locates spots within streaks; and 6) is automated and efficient.

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“…These must be characterized for further analysis of the sample, such as comparison across a set of gels. Currently, many commercial and academic 2-DE image analysis packages are available [1][2][3][4][5][6][7], each with an associated spot identification and characterization algorithm. One of the first steps in any spot detection algorithm is the segmentation of individual spots from the background.…”

Section: Introductionmentioning

confidence: 99%

“…Spot characterization algorithms can be divided into two categories: parametric and nonparametric. Nonparametric methods [6,[8][9][10][11] carry out various heuristic post-processing routines on the raw segmentation boundaries to delineate the spots. Spots are then represented by a set of meas-urements calculated over the detected spot regions.…”

Section: Introductionmentioning

confidence: 99%

Statistical models of shape for the analysis of protein spots in two‐dimensional electrophoresis gel images

2003

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In image analysis of two-dimensional electrophoresis gels, individual spots need to be identified and quantified. Two classes of algorithms are commonly applied to this task. Parametric methods rely on a model, making strong assumptions about spot appearance, but are often insufficiently flexible to adequately represent all spots that may be present in a gel. Nonparametric methods make no assumptions about spot appearance and consequently impose few constraints on spot detection, allowing more flexibility but reducing robustness when image data is complex. We describe a parametric representation of spot shape that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. Our method uses a model of shape based on the statistics of an annotated training set. The model allows new spot shapes, belonging to the same statistical distribution as the training set, to be generated. To represent spot appearance we use the statistically derived shape convolved with a Gaussian kernel, simulating the diffusion process in spot formation. We show that the statistical model of spot appearance and shape is able to fit to image data more closely than the commonly used spot parameterizations based solely on Gaussian and diffusion models. We show that improvements in model fitting are gained without degrading the specificity of the representation.

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A fast spot segmentation algorithm for two‐dimensional gel electrophoresis analysis

Cited by 17 publications

References 14 publications

The role of bioinformatics in two‐dimensional gel electrophoresis

The role of bioinformatics in two‐dimensional gel electrophoresis

A Computer-Based Technique for Automated Spot Detection in Proteomics Images

Statistical models of shape for the analysis of protein spots in two‐dimensional electrophoresis gel images

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