2007 IEEE 11th International Conference on Computer Vision 2007
DOI: 10.1109/iccv.2007.4409131
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Using the P<sup>n</sup> Potts model with learning methods to segment live cell images

Abstract: We present a segmentation method for live cell images, using graph cuts and learning methods. The images used here are particularly challenging because of the shared grey-level distributions of cells and background, which only differ by their textures, and the local imprecision around cell borders. We use the P n Potts model recently presented by Kohli et al. [9]: functions on higher-order cliques of pixels are included into the traditional Potts model, allowing us to account for local texture features, and to… Show more

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Cited by 11 publications
(11 citation statements)
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“…Biomedical image segmentation aims to find the boundaries of various biological structures, e.g. cells, chromosomes, genes, proteins and other sub-cellular components in various image types [14]. Light microscope techniques are often used, but the resulting images are frequently noisy, blurred, and of low contrast, making accurate segmentation difficult.…”
Section: Application In Biomedical Imagingmentioning
confidence: 99%
“…Biomedical image segmentation aims to find the boundaries of various biological structures, e.g. cells, chromosomes, genes, proteins and other sub-cellular components in various image types [14]. Light microscope techniques are often used, but the resulting images are frequently noisy, blurred, and of low contrast, making accurate segmentation difficult.…”
Section: Application In Biomedical Imagingmentioning
confidence: 99%
“…Image segmentation in biomedical imaging is aiming to find boundaries of various biological structures such as cells, chromosomes, genes, proteins and other sub-cellular components [1], [2], [3]. Due to the highly complex structures, semi-automatic (or interactive) methods allowing for a minimal user interaction are preferable as the identification of foreground regions requires expert knowledge.…”
Section: Introductionmentioning
confidence: 99%
“…Cell segmentation methods for phase contrast images can be categorized into region‐based approaches,() active contour methods,() energy minimization–based approaches,() image restoration–based approaches,() and machine learning–based approaches. ()…”
Section: Introductionmentioning
confidence: 99%
“…Cell segmentation methods for phase contrast images can be categorized into region-based approaches, 1-4 active contour methods, [5][6][7][8] energy minimization-based approaches, [9][10][11][12] image restoration-based approaches, [13][14][15] and machine learning-based approaches. [15][16][17][18][19][20][21][22] Region-based approaches include methods such as thresholding and watershed.…”
Section: Introductionmentioning
confidence: 99%