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Xujiong Ye; Gareth Beddoe; Greg Slabaugh (2010)
Publisher: Hindawi Limited
Journal: International Journal of Biomedical Imaging
Languages: English
Types: Article
Subjects: Research Article, R855-855.5, QA75, R895-920, G400 Computer Science, Article Subject, Medical technology, Medical physics. Medical radiology. Nuclear medicine
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.
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    • [1] S. A. Hojjatoleslami and J. Kittler, “Region growing: a new approach,” IEEE Transactions on Image Processing, vol. 7, no. 7, pp. 1079-1084, 1998.
    • [2] J. Dehmeshki, H. Amin, M. Valdivieso, and X. Ye, “Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach,” IEEE Transactions on Medical Imaging, vol. 27, no. 4, pp. 467-480, 2008.
    • [3] J. Yao, M. Miller, M. Franaszek, and R. M. Summers, “Colonic polyp segmentation in CT colonography-based on fuzzy clustering and deformable models,” IEEE Transactions on Medical Imaging, vol. 23, no. 11, pp. 1344-1352, 2004.
    • [4] J. J. Dijkers, C. Van Wijk, F. M. Vos et al., “Segmentation and size measurement of polyps in CT colonography,” in Proceedings of the 8th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '05), vol. 3749, pp. 712-719, 2005.
    • [5] L. Lu, A. Barbu, M. Wolf, J. Liang, M. Salganicoff, and D. Comaniciu, “Accurate polyp segmentation for 3D CT colongraphy using multi-staged probabilistic binary learning and compositional model,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), June 2008.
    • [6] Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222- 1239, 2001.
    • [7] Y. Zheng, K. Steiner, T. Bauer, J. Yu, D. Shen, and C. Kambhamettu, “Lung nodule growth analysis from 3D CT data with a coupled segmentation and registration framework,” in Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV '07), October 2007.
    • [8] Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, “Lazy snapping,” ACM Transactions on Graphics, vol. 23, no. 3, pp. 303-308, 2004.
    • [9] N. Xu, N. Ahuja, and R. Bansal, “Object segmentation using graph cuts based active contours,” Computer Vision and Image Understanding, vol. 107, no. 3, pp. 210-224, 2007.
    • [10] G. Slabaugh and G. Unal, “Graph cuts segmentation using an elliptical shape prior,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), pp. 1222-1225, September 2005.
    • [11] X. Liu, O. Veksler, and J. Samarabandu, “Graph cut with ordering constraints on labels and its applications,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), Anchorage, Alaska, USA, June 2008.
    • [12] Y. Zheng, C. Kambhamettu, T. Bauer, and K. Steiner, “Estimation of ground-glass opacity measurement in CT lung images,” in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '08), pp. 238-245, 2008.
    • [13] X. Ye, M. Siddique, A. Douiri, G. Beddoe, and G. Slabaugh, “Graph-cut based automatic segmentation of lung nodules using shape, intensity and spatial features,” in Proceedings of the 2nd International Workshop on Pulmonary Image Analysis, Held in Conjunction with the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI '09), 2009.
    • [14] O. Faugeras, Three-Dimensional Computer Vision: A Geometric View-Point, MIT Press, Cambridge, Mass, USA, 1993.
    • [15] O. Monga and S. Benayoun, “Using partial derivatives of 3D images to extract typical surface features,” Computer Vision and Image Understanding, vol. 61, no. 2, pp. 171-189, 1995.
    • [16] H. Yoshida and J. Na¨ppi, “Three-dimensional computeraided diagnosis scheme for detection of colonic polyps,” IEEE Transactions on Medical Imaging, vol. 20, no. 12, pp. 1261- 1274, 2001.
    • [17] D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002.
    • [18] R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison Wesley, San Francisco, Calif, USA, 2002.
    • [19] X. Ye, X. Lin, J. Dehmeshki, G. Slabaugh, and G. Beddoe, “Shape-based computer-aided detection of lung nodules in thoracic CT images,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 7, pp. 1810-1820, 2009.
    • [20] S. Geman and D. Geman, “Stochastic relaxation, gibbs distributions, and the bayesian restoration of images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, 1984.
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