Detection and segmentation in 2D gel electrophoresis images (original) (raw)
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A Spot Segmentation Approach for 2D Gel Electrophoresis Images Based on 2D Histograms
2010
Spot-Segmentation, an essential stage of processing 2D gel electrophoresis images, remains a challenging process. The available software programs and techniques fail to separate overlapping protein spots correctly and cannot detect low intensity spots without human intervention. This paper presents an original approach to spot segmentation in 2D gel electrophoresis images. The proposed approach is based on 2D-histograms of the aforementioned images. The conducted experiments in a set of 16-bit 2D gel electrophoresis images demonstrate that the proposed method is very effective and it outperforms existing techniques even when it is applied to images containing several overlapping spots as well as to images containing spots of various intensities, sizes and shapes.
Segmentation of two-dimensional gel electrophoresis images containing overlapping spots
IEEE EMBS International Conference on Information Technology Applications in Biomedicine, 2009
This work addresses the segmentation of two-dimensional polyacrylamide gel electrophoresis images containing overlapping protein spots. A novel segmentation approach is proposed, which is capable of detecting spot boundaries within the region of overlap. The proposed approach is based on the observation that the spot boundaries in the overlap region are associated with local intensity minima. The experimental evaluation of the
2011) “New approach for segmentation and quantification of twodimensional gel electrophoresis images
2016
Motivation: Detection of protein spots in two-dimensional gel electrophoresis images is a very complex task and current approaches addressing this problem still suffer from significant shortcomings. When quantifying a spot, most of the current software applications include a lot of background due to poor segmentation. Other software applications use a fixed window for this task, resulting in omission of part of the protein spot, or including background in the quantification. The approach presented here for the segmentation and quantification of two-dimensional gel electrophoresis images aims to minimize these problems. Results: Five sections from different gels are used to test the performance of the presented method concerning the detection of protein spots, and three gel sections are used to test the quantification of sixty protein spots. Comparisons with a state-of-the-art commercial software, and an academic state-of-the-art approach are presented. It is shown that the proposed ...
PROTEOMICS, 2003
A novel approach to spot detection for two-dimensional gel electrophoresis images using pixel value collection Separation of complex mixtures of proteins by two-dimensional gel electrophoresis (2-DE) is a fundamental component of current proteomic technology. Quantitative analysis of the images generated by digitization of such gels is critical for the identification of alterations in protein expression within a given biological system. Despite the availability of several commercially available software packages designed for this purpose, image analysis is extremely resource intensive, subjective and remains a major bottleneck. In addition to reducing throughput, the requirement for manual intervention results in the introduction of operator subjectivity, which can limit the statistical significance of the numerical data generated. A key requirement of image analysis is the accurate definition of protein spot boundaries using a suitable method of image segmentation. We describe a method of spot detection applicable to 2-DE image files using a segmentation method involving pixel value collection via serial analysis of the image through its range of density levels. This algorithm is reproducible, sensitive, accurate and primarily designed to be automatic, removing operator subjectivity. Furthermore, it is believed that this method may offer the potential for improved spot detection over currently available software.
2009
Two-dimensional gel-electrophoresis (2DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC). ...
2D electrophoresis image segmentation within a pixel-based framework
Chemometrics and Intelligent Laboratory Systems, 2015
Two-dimensional electrophoresis (2DE) is a traditional proteomics tool still used extensively to study differences in complex protein expression profiles between related biological samples. The methods can resolve thousands of intact proteins on a gel. However, the resulting image pattern is complex. Traditionally, each individual 2DE image, representing one replicate of one biological sample, is first segmented into its many different spots and the volume is quantified for each spot in each gel image; thereafter lists of protein spot volumes from different samples are collected for statistical analysis. This segmentation-before-analysis approach is known to cause considerable segmentation problems due to weak or overlapping protein spots, which in turn causes, for instance, missing values and other misrepresentations in the resulting spot volume table and hence problems in the statistical analysis. The pixel-based approach was introduced to solve some of the challenges inherited in the analysis of 2DE images, in particular, caused by early spot detection. The current paper follows a complete pixel-based approach from aligning to a resulting spot volume table, with several novel steps along the workflow. The workflow encompasses image pre-processing, pixel-based analyses, segmentation where the experimental design is utilised, and final data table reporting. Our approach employs optical flow estimation for alignment, and technical variation is removed prior to the statistical validation. The statistical data analyses are based on ANOVA adjusted by rotation testing and on resampled PLS regression at pixel level. A novel approach for segmentation is performed based on the statistical output performed on pixel level (regression coefficients and their p-values or t-values), and refolded to image representation (meta image). A double threshold is used to distinguish random false positive pixels from structured information of protein spots. Finally, the end user is presented a list of protein spots of interest at spot volume level rather than pixel level where the spot detection procedure has taken into account the relevance in light of the experimental design.
Two-dimensional gel-electrophoresis (2DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC). The results show that LPVC achieves similar segmentation results as PVC, but is much faster than PVC.
Comparative and functional genomics, 2007
Two-dimensional gel-electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a blob-shaped spot of grey level values. The spot detection, that is, the segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper, we propose a novel spot detector that is basically a morphology-based method with the use of a seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on our synthetic as well as on real gels with human samples from SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. A comparison of results is done with a method called pixel value collection (PVC). Since our algorithm efficiently uses local spot information, segments the spot by collecting pixel values and its affinity with PVC, we named it local pixel value collection (LPVC)....
A simple and effective detection technique of 2D electrophoresis image protein spots
2005
The proteomic analysis is a set of process involving modern analytical techniques: -Realization of 2D Electrophoresis-Gels (2DE-G). - Processing of 2DE-G image and location of new or modified proteins. - Peptidic identification by mass spectrometry. - Location in data bases of proteins whose peptidic card correlates with respect to that found. The 2DE-G image processing is a crucial stage because
Automatic segmentation and modelling of two-dimensional electrophoresis gels
1996
An important issue in the analysis of two-dimensional electrophoresis images is the detection and quantification of protein spots. In this paper we describe a new robust technique to segment and model the different spots present in the gels. For the segmentation a watershed technique is applied. For the quantification of the spots, a new spot model is constructed, based on diffusion principles. Besides the advantage of having a physical interpretation, the model is demonstrated to be superior to the commonly used Gaussian models.