Autonomous sub-image matching for two-dimensional electrophoresis gels using MaxRST algorithm

Lin, Daw-Tung

doi:10.1016/j.imavis.2010.01.004

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…As a first step, a 3 × 3 median filter is applied on the 2-D PAGE image, so as to tackle with the presence of noise. The application of the median filter on 2-D PAGE images has been supported in previous works such as [25].…”

Section: Morphology-based Protein Spot Detectionmentioning

confidence: 87%

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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Section: Morphology-based Protein Spot Detectionmentioning

confidence: 87%

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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“…The original image is pre-processed with a k×k median filter [36] aiming to reduce the side effects of noise on the following processes. The pre-processed image is scanned with parallel straight-line segments of variable lengths and multiple directions ( Fig.…”

Section: [Figure 2]mentioning

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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“…the four nearest neighbors (4NN) median filter is applied on the input 2D-GE image [16], so as to minimize the detection of background intensity peaks, 2. the image is dilated with a disk-shaped SE approximating the dominant roundish shape of protein spots. The typical size of a protein spot ranges from 20 to 300 pixels.…”

Section: Proposed Morphology-based Approachmentioning

confidence: 99%