Abstract
Cirrhosis will cause significant morphological changes on both liver and spleen. In this paper, we constructed not only the liver statistical shape models (SSM), but also the spleen SSM and a joint SSM of the liver and the spleen for a morphologic analysis of the cirrhotic liver in CT images. We also proposed a mode selection method based on both its accumulation contribution rate and its correlation with doctor’s opinions (labels). The classification performance for normal and abnormal livers is significantly improved by our proposed method. The classification accuracies for normal and cirrhotic livers are 88% and 90%, respectively.
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References
Anthony, P.P., et al.: The Morphology of Cirrhosis. Journal of Clinical Pathology 31, 395–414 (1978)
Kohara, S., Tateyama, T., Foruzan, A.H., Furukawa, A., Kanasaki, S., Wakamiya, M., Wei, X., Chen, Y.-W.: Preliminary Study on Statistical Shape Model Applied to Diagnosis of Liver Cirrhosis. In: Proc. of 2011 IEEE International Conference on Image Processing, Brussels, Belguim, pp. 2978–2981 (2011)
Chen, Y.-W., Uetani, M., Kohara, S., Tateyama, T., Han, X.-H., Furukawa, A., Kanasaki, S.: Application of Statistical Shape Model of the Liver in Classification of Cirrhosis. International Journal of Digital Content Technology and its Applications (in press, 2013)
Chen, Y.-W., Luo, J., Tateyama, T., Han, X.-H., Furukawa, A., Kanasaki, S., Jiang, H.: Statistical Shape Model of the Liver and Effective Mode Selection for Classification of Liver Cirrhosis. In: Proc. of 6th International Conference on New Trends in Information Science, Service Science and Data Mining (ISSDM 2012), Taiwan, pp. 446–449 (2012)
Linguraru, M.G., Li, Z., Shah, F., Chin, S., Summers, R.M.: Automated Liver Segmentation using a Normalized Probabilistic Atlas. In: Proc. of SPIE, vol. 7262, 72622R-2 (2009)
Lamecker, H., Lange, T., Seebaß, M.: Segmentation of the Liver using a 3D Statistical Shape Model. ZIB-Report 04-09 (2004)
Okada, T., Shimada, R., Hori, M., Nakamoto, M., Chen, Y.-W., Nakamaura, H., Sato, Y.: Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model. Academic Radiology V63.15, 1390–1403 (2008)
Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. In: Computer Graphics (Proceedings of SIGGRAPH 1987), vol. 21(4), pp. 163–170 (1987)
Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding In Non Rigid Image Registration 89(2-3), 114–141 (2003)
Luo, J., Chen, Y.-W., Tateyama, T., Han, X.-H., Furukawa, A., Kanasaki, S.: Pilot Study of Applying Shape Analysis to Liver Cirrhosis Diagnosis. In: ICIP 2013 (2013) (accepted)
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Chen, YW., Luo, J., Han, X., Tateyama, T., Furukawa, A., Kanasaki, S. (2013). A Morphologic Analysis of Cirrhotic Liver in CT Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_56
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DOI: https://doi.org/10.1007/978-3-642-39094-4_56
Publisher Name: Springer, Berlin, Heidelberg
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