Utilizing Disease-Specific Organ Shape Components for Disease Discrimination: Application to Discrimination of Chronic Liver Disease from CT Data

  • Dipti Prasad Mukherjee
  • Keisuke Higashiura
  • Toshiyuki Okada
  • Masatoshi Hori
  • Yen-Wei Chen
  • Noriyuki Tomiyama
  • Yoshinobu Sato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8149)

Abstract

We describe a method to capture disease-specific components in organ shapes. A statistical shape model, constructed by the principal component analysis (PCA) of organ shapes, is used to define the subspace representing inter-subject shape variability. The first PCA is applied to the datasets of healthy organ shapes to define the subspace of normal variability. Then, the datasets of diseased shapes are projected onto the orthogonal complement (OC) of the subspace of normal variability, and the second PCA is applied to the projected datasets to derive the subspace representing the disease-specific variability. To calculate the OC of an n-dimensional subspace, a novel closed-form formulation is developed. Experiments were performed to show that the support vector machine classification in the OC subspace better discriminated healthy and diseased liver shapes using 99 CT data. The effects of the number of training data and the difference in segmentation methods on the classification accuracy were evaluated to clarify the characteristics of the proposed method.

Keywords

Statistical shape model orthogonal complement support vector machine liver fibrosis computer-aided diagnosis 

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References

  1. 1.
    Thompson, P., Mega, M., Woods, R., Zoumalan, C., Lindshield, C., Blanton, R., Moussai, J., Holmes, C., Cummings, J., Toga, A.: Cortical Change in Alzheimer’s Disease Detected with a Disease-specific Population-based Brain Atlas. Cerebral Cortex 11(1), 1–16 (2001)CrossRefGoogle Scholar
  2. 2.
    Heimann, T., Meinzer, H.: Statistical Shape Models for 3D Medical Image Segmentation: A review. Med. Image Anal. 13(4), 543–563 (2009)CrossRefGoogle Scholar
  3. 3.
    Dacheng, T., Xiaoou, T.: Orthogonal Complement Component Analysis for Positive Samples in SVM Based Relevance Feedback Image Retrieval. Computer Vision and Pattern Recognition 2, 586–591 (2004)Google Scholar
  4. 4.
    Golland, P., Grimson, E., Shenton, M., Kikinis, R.: Detection and analysis of statistical differences in anatomical shape. Med. Image Anal. 9(1), 69–86 (2005)CrossRefGoogle Scholar
  5. 5.
    Goshima, S., Kanematsu, M., Koayashi, T., Furukawa, T., Zhang, X., Fujita, H., Watanabe, H., Kondo, H., Moriyama, N., Bae, K.: Staging Hepatic Fibrosis: Computer-Aided Analysis of Hepatic Contours on Gadolinium Ethoxybenzyl Diethylenetriaminepentaacetic Acid-Enhanced Hepatocyte-Phase Magnetic Resonance Imaging. Hepatology 55(1), 328–329 (2012)CrossRefGoogle Scholar
  6. 6.
    Badeau, R., Richard, G., Bertrand, D.: Fast and stable YAST algorithm for principal and minor subspace tracking. IEEE Trans. Signal Process. 56(8), 3437–3446 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Higham, N.: Computing the polar decompositions-with applications. SIAM Journal on Scientific and Statistical Computing 7(4), 1160–1174 (1986)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Non-rigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  9. 9.
    Okada, T., Shimada, R., Sato, Y., Hori, M., Yokota, K., Nakamoto, M., Chen, Y.W., Nakamura, H., Tamura, S.: Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model. Acad. Radiol. 15(11), 1390–1403 (2008)CrossRefGoogle Scholar
  10. 10.
    Ichida, F., Tsuji, T., Omata, M., Ichida, T., Inoue, K., Kamimura, T., Yamada, G., Hino, K., Yokosuka, O., Suzuki, H.: New Inuyama classification; new criteria for histological assessment of chronic hepatitis. International Hepatology Communications 6(2), 112–119 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dipti Prasad Mukherjee
    • 1
    • 2
  • Keisuke Higashiura
    • 3
  • Toshiyuki Okada
    • 2
  • Masatoshi Hori
    • 2
  • Yen-Wei Chen
    • 3
  • Noriyuki Tomiyama
    • 2
  • Yoshinobu Sato
    • 2
  1. 1.Indian Statistical InstituteKolkataIndia
  2. 2.Department of Radiology, Graduate School of MedicineOsaka UniversityJapan
  3. 3.Graduate School of Information Science and EngineeringRitsumeikan UniversityJapan

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