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Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels.

Ye X, Beddoe G, Slabaugh G - Int J Biomed Imaging (2010)

Bottom Line: The mean shift superpixels increase the robustness of the result while reducing the computation time.We assume that the lesion is part spherical, resulting in high shape index values in a part of the lesion.The joint spatial-intensity-shape features provide a powerful cue for successful segmentation of lesions adjacent to structures of similar intensity but different shape, as well as lesions exhibiting partial volume effect.

View Article: PubMed Central - PubMed

Affiliation: R&D Department, Medicsight PLC, 66 Hammersmith Road, London W14 8UD, UK.

ABSTRACT
This paper presents a new, automatic method of accurately extracting lesions from CT data. It first determines, at each voxel, a five-dimensional (5D) feature vector that contains intensity, shape index, and 3D spatial location. Then, nonparametric mean shift clustering forms superpixels from these 5D features, resulting in an oversegmentation of the image. Finally, a graph cut algorithm groups the superpixels using a novel energy formulation that incorporates shape, intensity, and spatial features. The mean shift superpixels increase the robustness of the result while reducing the computation time. We assume that the lesion is part spherical, resulting in high shape index values in a part of the lesion. From these spherical subregions, foreground and background seeds for the graph cut segmentation can be automatically obtained. The proposed method has been evaluated on a clinical CT dataset. Visual inspection on different types of lesions (lung nodules and colonic polyps), as well as a quantitative evaluation on 101 solid and 80 GGO nodules, both demonstrate the potential of the proposed method. The joint spatial-intensity-shape features provide a powerful cue for successful segmentation of lesions adjacent to structures of similar intensity but different shape, as well as lesions exhibiting partial volume effect.

No MeSH data available.


Related in: MedlinePlus

Schematic diagram of different types of superpixels: foreground (green) superpixels, background (red) superpixels, and uncertain (gray) superpixels.
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fig6: Schematic diagram of different types of superpixels: foreground (green) superpixels, background (red) superpixels, and uncertain (gray) superpixels.

Mentions: We are given the initial foreground {MmF} and background regions{MtB}, automatically calculated from the previous section, where m and t are the superpixel indices for initial foreground and background, respectively. Figure 6 shows the schematic diagram for different types of superpixels, for example, foreground, background, and uncertain superpixels. For each superpixel i, the unary term is defined as


Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels.

Ye X, Beddoe G, Slabaugh G - Int J Biomed Imaging (2010)

Schematic diagram of different types of superpixels: foreground (green) superpixels, background (red) superpixels, and uncertain (gray) superpixels.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2967838&req=5

fig6: Schematic diagram of different types of superpixels: foreground (green) superpixels, background (red) superpixels, and uncertain (gray) superpixels.
Mentions: We are given the initial foreground {MmF} and background regions{MtB}, automatically calculated from the previous section, where m and t are the superpixel indices for initial foreground and background, respectively. Figure 6 shows the schematic diagram for different types of superpixels, for example, foreground, background, and uncertain superpixels. For each superpixel i, the unary term is defined as

Bottom Line: The mean shift superpixels increase the robustness of the result while reducing the computation time.We assume that the lesion is part spherical, resulting in high shape index values in a part of the lesion.The joint spatial-intensity-shape features provide a powerful cue for successful segmentation of lesions adjacent to structures of similar intensity but different shape, as well as lesions exhibiting partial volume effect.

View Article: PubMed Central - PubMed

Affiliation: R&D Department, Medicsight PLC, 66 Hammersmith Road, London W14 8UD, UK.

ABSTRACT
This paper presents a new, automatic method of accurately extracting lesions from CT data. It first determines, at each voxel, a five-dimensional (5D) feature vector that contains intensity, shape index, and 3D spatial location. Then, nonparametric mean shift clustering forms superpixels from these 5D features, resulting in an oversegmentation of the image. Finally, a graph cut algorithm groups the superpixels using a novel energy formulation that incorporates shape, intensity, and spatial features. The mean shift superpixels increase the robustness of the result while reducing the computation time. We assume that the lesion is part spherical, resulting in high shape index values in a part of the lesion. From these spherical subregions, foreground and background seeds for the graph cut segmentation can be automatically obtained. The proposed method has been evaluated on a clinical CT dataset. Visual inspection on different types of lesions (lung nodules and colonic polyps), as well as a quantitative evaluation on 101 solid and 80 GGO nodules, both demonstrate the potential of the proposed method. The joint spatial-intensity-shape features provide a powerful cue for successful segmentation of lesions adjacent to structures of similar intensity but different shape, as well as lesions exhibiting partial volume effect.

No MeSH data available.


Related in: MedlinePlus