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). The results show that LPVC
achieves similar segmentation results as PVC, but is much faster than PVC.
Computer vision algorithms that use color information require color constant images to operate correctly. Color constancy of the images is usually achieved in two steps: first the illuminant is detected and then image is transformed with the chromatic adaptation transform (CAT). Existing CAT methods use a single transformation matrix for all the colors of the input image. The method proposed in this paper requires multiple corresponding color pairs between source and target illuminants given by patches of the Macbeth color checker. It uses Delaunay triangulation to divide the color gamut of the input image into small triangles. Each color of the input image is associated with the triangle containing the color point and transformed with a full linear model associated with the triangle. Full linear model is used because diagonal models are known to be inaccurate if channel color matching functions do not have narrow peaks. Objective evaluation showed that the proposed method outperforms existing CAT methods by more than 21%; that is, it performs statistically significantly better than other existing methods.
Abstract:Two-dimensional gel electrophoresis (2-DE) images show the expression levels of several hundred of proteins where each protein is represented as a blob shaped spot of grey level values. The spot detection, i.e. 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 real time spot detector that is basically a morphology based method with use of seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on gels with human samples in SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. The average time to process the image is less than a second, while the results are very intuitive for human perception and as such they help the user to focus on important parts of the gel in the subsequent processing. In gels with less than 50 identified spots as proteins (proteins that compose a proteome) in the mentioned database, the algorithm detects all obvious spots.
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