The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.05 - May (2010 vol.32)
pp: 799-814
Dimitris Arabadjis , National Techncal University of Athens , Athens
Panayiotis Rousopoulos , National Techncal University of Athens, Athens
Constantin Papaodysseus , National Technical University of Athens, Athens
Michalis Panagopoulos , National Techncal University of Athens, Athens
Panayiota Loumou , National Techncal University of Athens, Athens
Georgios Theodoropoulos , Agricultural University of Athens, Athens
ABSTRACT
A novel methodology is introduced here that exploits 2D images of arbitrary elastic body deformation instances so as to quantify mechanoelastic characteristics that are deformation invariant. Determination of such characteristics allows for developing methods offering an image of the undeformed body. General assumptions about the mechanoelastic properties of the bodies are stated which lead to two different approaches for obtaining bodies' deformation invariants. One was developed to spot a deformed body's neutral line and its cross sections, while the other solves deformation PDEs by performing a set of equivalent image operations on the deformed body images. Both of these processes may furnish a body-undeformed version from its deformed image. This was confirmed by obtaining the undeformed shape of deformed parasites, cells (protozoa), fibers, and human lips. In addition, the method has been applied to the important problem of parasite automatic classification from their microscopic images. To achieve this, we first apply the previous method to straighten the highly deformed parasites, and then, apply a dedicated curve classification method to the straightened parasite contours. It is demonstrated that essentially different deformations of the same parasite give rise to practically the same undeformed shape, thus confirming the consistency of the introduced methodology. Finally, the developed pattern recognition method classifies the unwrapped parasites into six families, with an accuracy rate of 97.6 percent.
INDEX TERMS
Deformation invariant elastic properties, automatic curve classification, parasite automatic identification, straightening deformed objects, image analysis, elastic deformation, pattern classification techniques.
CITATION
Dimitris Arabadjis, Panayiotis Rousopoulos, Constantin Papaodysseus, Michalis Panagopoulos, Panayiota Loumou, Georgios Theodoropoulos, "A General Methodology for the Determination of 2D Bodies Elastic Deformation Invariants: Application to the Automatic Identification of Parasites", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 5, pp. 799-814, May 2010, doi:10.1109/TPAMI.2009.70
REFERENCES
[1] G. Adam, P.J. Xiaoyi, D.B. Horst, D.W. Kevin, and R.B. Andrew, "An Experimental Comparison of Range Image Segmentation Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 7, pp. 673-689, July 1996.
[2] S.J. Ahn, W. Rauh, H.S. Cho, and H.J. Warnecke, "Orthogonal Distance Fitting of Implicit Curves and Surfaces," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 620-638, May 2002.
[3] R.W. Brockett and P. Maragos, "Evolution Equations for Continuous-Scale Morphological Filtering," IEEE Trans. Signal Processing, vol. 42, no. 12, pp. 3377-3386, Dec. 1994.
[4] G.E. Christensen, R.D. Rabbitt, and M.I. Miller, "Deformable Templates Using Large Deformation Kinematics," IEEE Trans. Image Processing, vol. 5, no. 10, pp. 1435-1447, Oct. 1996.
[5] D. Craig and C. Gotsman, "Fitting Curves and Surfaces with Constrained Implicit Polynomials," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 1, pp. 31-41, Jan. 1999.
[6] D. Cremers, S.J. Osher, and S. Soatto, "Kernel Density Estimation and Intrinsic Alignment for Shape Priors in Level Set Segmentation," Int'l J. Computer Vision, vol. 69, no. 3, pp. 335-351, 2006.
[7] K.F. Lai and R.T. Chin, "On Modelling, Extraction, Detection and Classification of Deformable Contours from Noisy Images," Image and Vision Computing, vol. 16, no. 1, pp. 55-62, 1998.
[8] D. Lee, S. Baek, and K. Sung, "Modified k-Means Algorithm for Vector Quantizer Design," IEEE Signal Processing Letters, vol. 4, no. 1, pp. 2-4, Jan. 1997.
[9] A. Manduca, V. Dutt, D.T. Borup, R. Muthupillai, R.L. Ehman, and J.F. Greenleaf, "Reconstruction of Elasticity and Attenuation Maps in Shear Wave Imaging: An Inverse Approach," Proc. Int'l Conf. Medical Image Computing and Computer-Assisted Interventation, pp. 606-613, 1998.
[10] L.W. McMurtry, M.J. Donaghy, A. Vlassoff, and P.G.C. Douch, "Distinguishing Morphological Features of the Third Larval Stage of Ovine Trichostrongylus SPP," Veterinary Parasitology, vol. 90, nos. 1/2, pp. 73-81, 2000.
[11] H.T. Nguyen, M. Worring, and R. van den Boomgaard, "Watersnakes: Energy-Driven Watershed Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 3, pp. 330-342, Mar. 2003.
[12] T. Panagopoulos, C. Papaodysseus, M. Exarhos, C. Triantafillou, G. Roussopoulos, and P. Roussopoulos, "Prehistoric Wall-Paintings Reconstruction Using Image Pattern Analysis and Curve Fitting," WSEAS Trans. Electronics, vol. 1, no. 1, pp. 108-113, Jan. 2004.
[13] C. Papaodysseus, M. Exarhos, T. Panagopoulos, C. Triantafillou, G. Roussopoulos, A. Pantazi, V. Loumos, D. Fragoulis, and C. Doumas, "Identification of Geometrical Shapes in Paintings and Its Application to Demonstrate the Foundations of Geometry in 1650 BC," IEEE Trans. Image Processing, vol. 14, no. 7, pp. 862-873, July 2005.
[14] X. Pennec, R. Stefanescu, V. Arsigny, P. Fillard, and N. Ayache, "Riemannian Elasticity: A Statistical Regularization Framework for Non-Linear Registration," Proc. Int'l Conf. Medical Image Computing and Computer-Assisted Interventation, pp. 943-950, 2005.
[15] R. Roman-Roldan, J.F. Gomez-Lopera, C. Atae-Allah, J. Martinez-Aroza, and P.L. Luque-Escamilla, "A Measure of Quality for Evaluating Methods of Segmentation and Edge Detection," Pattern Recognition, vol. 34, no. 5, pp. 969-980, May 2001.
[16] C. DiRuberto, A. Dempster, S. Khan, and B. Jarra, "Analysis of Infected Blood Cell Images Using Morphological Operators," Image and Vision Computing, vol. 20, no. 2, pp. 133-146, 2002.
[17] D. Terzopoulos, A. Witkin, and M. Kass, "Symmetry-Seeking Models and 3D Object Reconstruction," Int'l J. Computer Vision, vol. 1, no. 3, pp. 211-221, 1988.
[18] G. Theodoropoulos, V. Loumos, C. Anagnostopoulos, E. Kayafas, and B. Martinez-Gonzales, "A Digital Image Analysis and Neural Network Based System for Identification of Third-Stage Parasitic Strongyle Larvae from Domestic Animals," Computer Methods and Programs in Biomedicine, vol. 62, no. 2, pp. 69-76, 2000.
[19] A. Trouve, "Diffeomorphisms Groups and Pattern Matching in Image Analysis," Int'l J. Computer Vision, vol. 28, no. 3, pp. 213-221, 1998.
[20] C.W. Washington and M.I. Miga, "Modality Independent Elastography (MIE): A New Approach to Elasticity Imaging," IEEE Trans. Medical Imaging, vol. 23, no. 9, pp. 1117-1128, Sept. 2004.
31 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool