Knowledge Based Active Partition Approach for Heart Ventricle Recognition

  • Arkadiusz TomczykEmail author
  • Piotr S. Szczepaniak
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 578)


In the paper a method of automatic localization of heart ventricles in CT images is presented. Analysis of their shape can be an important element of pulmonary embolism diagnosis. For that purpose active partitions, a generalization of active contour approach, was used with superpixel representation of image content. Active partitions, similarly to active contours, possess a natural ability to incorporate external experience into object localization process. It means that not only information contained in the image itself but also experience of the radiologist and the medical knowledge can be used to improve segmentation results.


Active contours Active partitions Superpixels Medical imaging Heart ventricles Intelligent segmentation 



This project has been partly funded with support from National Science Centre, Republic of Poland, decision number DEC-2012/05/D/ST6/03091. Authors would like to also express their gratitude to Mr Cyprian Wolski, MD, from the Department of Radiology of Barlicki University Hospital in Lodz for making heart images available and sharing his medical knowledge.


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2281 (2012)CrossRefGoogle Scholar
  2. 2.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vision 22(1), 61–79 (2000)CrossRefzbMATHGoogle Scholar
  3. 3.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models - their training and application. CVGIP Image Underst. 61(1), 8–59 (1994)Google Scholar
  4. 4.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). doi: 10.1007/BFb0054760 CrossRefGoogle Scholar
  5. 5.
    Grzeszczuk, R., Levin, D.: Brownian strings: segmenting images with stochastically deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 19(10), 1100–1113 (1997)CrossRefGoogle Scholar
  6. 6.
    Ivins, J., Porrill, J.: Active region models for segmenting medical images. In: IEEE Transactions on Image Processing, pp. 227–231 (1994)Google Scholar
  7. 7.
    Kass, M., Witkin, W., Terzopoulos, S.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–333 (1988)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Siddiqi, K., Lauziere, Y., Tannenbaum, A., Zucker, S.: Area and length-minimizing flows for shape segmentation. IEEE Trans. Image Process. 7(3), 433–443 (1997)CrossRefGoogle Scholar
  10. 10.
    Tomczyk, A., Szczepaniak, P.S.: Adaptive potential active contours. Pattern Anal. Appl. 14, 425–440 (2011a)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Tomczyk, A., Szczepaniak, P.S.: Knowledge extraction for heart image segmentation. In: Burduk, R., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds.) Computer Recognition Systems 4. Advances in Intelligent and Soft Computing, vol. 95, pp. 579–586. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Institute of Information TechnologyLodz University of TechnologyLodzPoland

Personalised recommendations