A General Methodology for the Determination of 2D Bodies Elastic Deformation Invariants: Application to the Automatic Identification of Parasites

作者：Arabadjis Dimitris^*; Rousopoulos Panayiotis; Papaodysseus Constantin; Panagopoulos Michalis; Loumou Panayiota; Theodoropoulos Georgios

来源：IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 799-814.

DOI：10.1109/TPAMI.2009.70

• Deformation invariant elastic properties
• automatic curve classification
• parasite automatic identification
• straightening deformed objects
• image analysis
• elastic deformation
• pattern classification techniques

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.

• 出版日期2010-5