Unsupervised 2D gel electrophoresis image segmentation based on active contours

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.

► Proposed segmentation scheme is the first to exploit the advantages 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.
► Proposed segmentation scheme is unsupervised providing an alternate to the laborious, error-prone process of manual editing.
► Experiments are performed on real and synthetic 2D-GE images, so as to allow qualitative and quantitative evaluation.
► Proposed segmentation scheme outperforms state-of-the-art commercial software packages in terms of segmentation quality.