Viewpoint Recognition in Cardiac CT Images

  • Mehdi MoradiEmail author
  • Noel C. Codella
  • Tanveer Syeda-Mahmood
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9126)


Position and orientation information is often lacking in DICOM datasets. This creates a need for human involvement or computationally expensive 3D processing for any analytical tool, such as a software-based cognitive assistant, to determine the viewpoint of an input 2D image. We report a solution for cardiac CT viewpoint recognition to identify the desired images for a specific view and subsequent processing and anatomy recognition. We propose a new set of features to describe the global binary pattern of cardiac CT images characterized by the highly attenuating components of the anatomy in the image. We also use five classic image texture and edge feature sets and devise a classification approach based on SVM classification, class likelihood estimation, and majority voting, to classify 2D cardiac CT images into one of six viewpoint categories that include axial, sagittal, coronal, two chamber, four chamber, and short axis views. We show that our approach results in an accuracy of 99.4 % in correct labeling of the viewpoints.


Support Vector Machine Random Forest Local Binary Pattern Cardiac Compute Tomography Local Binary Pattern Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Budoff, M.J., Achenbach, S., Blumenthal, R.S., Carr, J.J., Goldin, J.G., Greenland, P., Guerci, A.D., Lima, J.A., Rader, D.J., Rubin, G.D., Shaw, L.J., Wiegers, S.E.: Assessment of coronary artery disease by cardiac computed tomography. Circulation 114, 1761–1791 (2006)CrossRefGoogle Scholar
  2. 2.
    Syeda-Mahmood, T., Wang, F., Beymer, D., Amir, A., Richmond, M., Hashmi, S.: Aalim: Multimodal mining for cardiac decision support. Comput. Cardiol. 34, 209–212 (2007)Google Scholar
  3. 3.
    Gueld, M.O., Kohnen, M., Keysers, D., Schubert, H., Wein, B.B., Bredno, J., Lehmann, T.M.: Quality of DICOM header information for image categorization. In: Proceedings of SPIE Medical Imaging, vol. 4685, pp. 280–287 (2002)Google Scholar
  4. 4.
    Yoshimura, H., Inoue, Y., Tanaka, H., Fujita, N., Hirabuki, N., Narumi, Y., Nakamura, H.: Operating data and unsolved problems of the dicom modality worklist: an indispensable tool in an electronic archiving environment. Radiat. Med. 21(2), 68–73 (2003)Google Scholar
  5. 5.
    Park, J., Georgescu, S.Z.B., Simopoulos, J., Otsuki, J., Comaniciu, D.: Automatic cardiac view classification of echocardiogram. In: ICCV, pp. 1–8 (2007)Google Scholar
  6. 6.
    Kumar, R., Wang, F., Beymer, D., Syeda-Mahmood, T.: Echocardiogram view classification using edge filtered scale-invariant motion features. In: IEEE CVPR, pp. 723–730 (2009)Google Scholar
  7. 7.
    Halpern, E.: Clinical Cardiac CT, 2nd edn. Thieme Medical Publishers Inc., USA (2011)Google Scholar
  8. 8.
    Top, A., Hamarneh, G., Abugharbieh, R.: Active learning for interactive 3D image segmentation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 603–610. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  9. 9.
    Cao, L., Chang, S., Codella, N., Cotton, C., Ellis, D., Gong, L., Hill, M., Hua, G., Kender, J., Merler, M., Mu, Y., Smith, J.: IBM Research and Columbia University TRECVID-2012 Multimedia Event Detection (MED), Multimedia Event Recounting (MER), and Semantic Indexing (SIN) Systems. In: NIST TRECVID Workshop, pp. 1–18 (2012)Google Scholar
  10. 10.
    Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  11. 11.
    Liao, P.S., Chen, T.S., Chung, P.C.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17, 51–59 (2001)Google Scholar
  12. 12.
    Platt, J.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv. Large Margin Classifiers 10(3), 61–74 (1999)Google Scholar
  13. 13.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of the Neural Information Processing Systems (NIPS), pp. 1–9 (2012)Google Scholar
  14. 14.
    Codella, N., Connell, J., Pankanti, S., Merler, M., Smith, J.R.: Automated medical image modality recognition by fusion of visual and text information. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part II. LNCS, vol. 8674, pp. 487–495. Springer, Heidelberg (2014) Google Scholar
  15. 15.
    Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 18. ACM, New York (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mehdi Moradi
    • 1
    Email author
  • Noel C. Codella
    • 2
  • Tanveer Syeda-Mahmood
    • 1
  1. 1.IBM Almaden Research CenterSan JoseUSA
  2. 2.IBM Thomas J. Watson Research CenterYorktown HeightsUSA

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