Computer analysis of two‐dimensional electrophoresis gels: A new segmentation and modeling algorithm

Bettens, E.; Scheunders, Paul; Dyck, D. Van; Moëns, Luc; Osta, Peter Van

doi:10.1002/elps.1150180523

Cited by 41 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both the main-body and the inner-dip 3D curves are expressed as the diffusion function C(x,y) proposed by Bettens et al [20], and therefore resemble the 3D Gaussian or plateau curve. Moreover, their orientation is opposite; the base of the main-body of the spot-model is down and its peak is up, while the base of the inner-dip of the spot-model is up and its peak is down (Fig.2).…”

Section: A Morphological Models For a Microarray Spot And Its Comparmentioning

confidence: 99%

Microarray image segmentation using spot morphological model

Maroulis

Zacharia

2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

View full text Add to dashboard Cite

Abstract-The up-to-date segmentation techniques and software programs for microarray image segmentation require human intervention which in turn may detrimentally affect the biological conclusions reached during microarray experiments. In this paper, an automatic approach for segmenting microarray images, based on the morphological modeling of spots, is presented. The conducted experiments have shown that the proposed approach is very effective even when it is applied to noisy images as well as to images containing spots of various shapes and intensities.

show abstract

Section: A Morphological Models For a Microarray Spot And Its Comparmentioning

confidence: 99%

Microarray image segmentation using spot morphological model

Maroulis

Zacharia

2009

2009 9th International Conference on Information Technology and Applications in Biomedicine

View full text Add to dashboard Cite

show abstract

“…Such manual operations have serious effects on the estimation of the spot volume and the location of the spot centroid (center of gravity). Recently, other methods have been applied, such as watershed transformation 5) , which can segment spots in 2-DE gel [6][7][8] , but cannot segment spot regions if the spots are connected to one another by the saturated black spots.…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of the silver staining gel image of 2-DE for protein spots segmentation

et al. 2010

View full text Add to dashboard Cite

SUMMARYA novel image analysis method combined with recently developed powerful computing hardware is proposed, in which the time course for the image data of silver staining gel taken during the developmental process was used for the analysis of protein spots in two-dimensional polyacrylamide gel electrophoresis. The alignment procedure of the time course gel images using the landmark spots of plastic fragments was a critical step in the precise analysis of protein spots. The merged, or adjacent, protein spots that inhibit the quantification and identification of the proteins were successfully segmented from regions that were automatically determined from the previous time gel image, which effectively removed operator subjectivity. Furthermore, the effect of termination time of the developing process on the gel image analysis was not a concern, because the present analysis used the whole-time course of the gel image.

show abstract

“…This model is assumed to provide a good representation of most spots present in most gel images. However, it has been shown that Gaussian models produce an inadequate fit to some protein spots, most notably large volume, saturated spots [1]. Figure 2(b) shows an example of a high volume protein spot exhibiting a saturated, 'flat-top' shape.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 2(b) shows an example of a high volume protein spot exhibiting a saturated, 'flat-top' shape. Bettens [1] addressed this shortcoming by proposing a model based on the physics of the spot formation process. Protein spots are formed by a diffusion process, which is only adequately represented by a Gaussian when the initial concentration distribution occupied by the sample has a small area.…”

Section: Introductionmentioning

confidence: 99%

Automatic Construction of Statistical Shape Models for Protein Spot Analysis in Electrophoresis Gels

Rogers¹,

Graham²,

Tonge³

2003

Procedings of the British Machine Vision Conference 2003

View full text Add to dashboard Cite

Proteomics research relies heavily on electrophoresis gels, which are complex images containing many protein 'spots'. The identification and quantification of these spots is a bottleneck in the proteomics workflow. We describe a statistical model of protein spot appearance that is both general enough to represent unusual spots, and specific enough to introduce constraints on the interpretation of complex images. We propose a robust method of automatic model construction that is used to circumvent manual model construction which is subjective and time-consuming. We show that the statistical model of spot appearance is able to fit to image data more closely than the commonly used spot parameterisations which are based solely on Gaussian and diffusion formulations.

show abstract

Computer analysis of two‐dimensional electrophoresis gels: A new segmentation and modeling algorithm

Cited by 41 publications

References 12 publications

Microarray image segmentation using spot morphological model

Microarray image segmentation using spot morphological model

Dynamic analysis of the silver staining gel image of 2-DE for protein spots segmentation

Automatic Construction of Statistical Shape Models for Protein Spot Analysis in Electrophoresis Gels

Contact Info

Product

Resources

About