Unsupervised 2D gel electrophoresis image segmentation based on active contours

Authors:
Michalis A. Savelonas;Eleftheria A. Mylona;Dimitris Maroulis
Affiliations:
Department of Informatics and Telecommunications, University of Athens, 15784 Panepistimioupolis, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, 15784 Panepistimioupolis, Athens, Greece;Department of Informatics and Telecommunications, University of Athens, 15784 Panepistimioupolis, Athens, Greece
Venue:
Pattern Recognition
Year:
2012

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Abstract

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 spot-targeted 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.