Abstract
In this paper, an image segmentation framework is proposed by unifying the techniques of spectral clustering and graph-cutting to address the difficult problem of breast lesion demarcation in sonography. In order to alleviate the effect of speckle noise and posterior acoustic shadows, the ROI of a sonogram is mapped to a specific eigen-space as an eigenmap by a constrained spectral clustering scheme. The eigen-mapping is boosted with the incorporation of partial grouping setting and then provide a useful preliminary aggregation based on intensity affinity. Following that, an iterative graph cut framework is carried out to identify the object of interest in the projected eigenmap. The proposed segmentation algorithm is evaluated with four sets of manual delineations on 110 breast ultrasound images. The experiment results corroborates that the boundaries derived by the proposed algorithm are comparable to manual delineations and hence can potentially provide reliable morphological information of a breast lesion.
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References
Cheng, J.Z., Chou, Y.H., Huang, C.S., Chang, Y.C., Tiu, C.M., Chen, K.W., Chen, C.M.: Computer-Aided US Diagnosis of Breast Lesions by Using Cell-based Contour Grouping. Radiology 255, 746–754 (2010)
Chen, C.M., Chou, Y.H., Han, K.C., Hung, G.S., Tiu, C.M., Chiou, H.J., Chiou, S.Y.: Breast lesions on sonograms: Computer-aided diagnosis with nearly setting-independent features and artificial neural networks. Radiology 226, 504–514 (2003)
Stavros, A.T., Thickman, D., Rapp, C.L., Dennis, M.A., Parker, S.H., Sisney, G.A.: Solid Breast Nodules - Use of Sonography to Distinguish Benign and Malignant Lesions. Radiology 196, 123–134 (1995)
Noble, J.A.: Ultrasound image segmentation and tissue characterization. Proceedings of the Institution of Mechanical Engineers Part H-Journal of Engineering in Medicine 224, 307–316 (2010)
Chen, C.M., Chou, Y.H., Chen, C.S.K., Cheng, J.Z., Ou, Y.F., Yeh, F.C., Chen, K.W.: Cell-competition algorithm: A new segmentation algorithm for multiple objects with irregular boundaries in ultrasound images. Ultrasound in Medicine and Biology 31, 1647–1664 (2005)
Cheng, J.Z., Chen, C.M., Chou, Y.H., Chen, C.S.K., Tiu, C.M., Chen, K.W.: Cell-based two-region competition algorithm with a map framework for boundary delineation of a series of 2D ultrasound images. Ultrasound in Medicine and Biology 33, 1640–1650 (2007)
Sahiner, B., Chan, H.P., Roubidoux, M.A., Hadjiiski, L.M., Helvie, M.A., Paramagul, C., Bailey, J., Nees, A.V., Blane, C.: Malignant and benign breast masses on 3D US volumetric images: Effect of computer-aided diagnosis on radiologist accuracy. Radiology 242, 716–724 (2007)
Chang, R.F., Wu, W.J., Moon, W.K., Chen, D.R.: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Research and Treatment 89, 179–185 (2005)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10, 266–277 (2001)
Archip, N., Rohling, R., Cooperberg, P., Tahmasebpour, H.: Ultrasound image segmentation using spectral clustering. Ultrasound in Medicine and Biology 31, 1485–1497 (2005)
Noble, J.A., Boukerroui, D.: Ultrasound image segmentation: A survey. IEEE Transactions on Medical Imaging 25, 987–1010 (2006)
Yu, S.X., Shi, J.B.: Grouping with Bias. Neural Information Processing Systems (2001)
Yu, S.X., Shi, J.B.: Segmentation given partial grouping constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 173–183 (2004)
Rother, C., Kolmogorov, V., Blake, A.: “GrabCut” - Interactive foreground extraction using iterated graph cuts. Acm Transactions on Graphics 23, 309–314 (2004)
Talbot, J., Xu, X.: Implementing GrabCut. Brigham Young University (2006)
Shi, J.B., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)
Madabhushi, A., Metaxas, D.N.: Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Transactions on Medical Imaging 22, 155–169 (2003)
Cour, T., Benezit, F., Shi, J.B.: Spectral segmentation with multiscale graph decomposition. In: CVPR 2005, Washington, DC, USA, pp. 1124–1131 (2005)
Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images. In: International Conference on Computer Vision, vol. I, pp. 105–112 (2001)
Imoverlay and imagesc | Steve on Image Processing, http://blogs.mathworks.com/steve/2007/07/20/imoverlay-and-imagesc/
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1124–1137 (2004)
Chalana, V., Linker, D.T., Haynor, D.R., Kim, Y.M.: A multiple active contour model for cardiac boundary detection on echocardiographic sequences. IEEE Transactions on Medical Imaging 15, 290–298 (1996)
Chalana, V., Kim, Y.M.: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Transactions on Medical Imaging 16, 642–652 (1997)
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Tsou, CH., Chen, JH., Cheng, JZ., Chen, CM. (2010). Spectral Aggregation Based on Iterative Graph Cut for Sonographic Breast Image Segmentation . In: Liao, H., Edwards, P.J."., Pan, X., Fan, Y., Yang, GZ. (eds) Medical Imaging and Augmented Reality. MIAR 2010. Lecture Notes in Computer Science, vol 6326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15699-1_40
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DOI: https://doi.org/10.1007/978-3-642-15699-1_40
Publisher Name: Springer, Berlin, Heidelberg
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