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

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dc.contributor.author Arabadjis, D en
dc.contributor.author Rousopoulos, P en
dc.contributor.author Papaodysseus, C en
dc.contributor.author Panagopoulos, M en
dc.contributor.author Loumou, P en
dc.contributor.author Theodoropoulos, G en
dc.date.accessioned 2014-03-01T01:32:26Z
dc.date.available 2014-03-01T01:32:26Z
dc.date.issued 2010 en
dc.identifier.issn 0162-8828 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/20135
dc.subject Deformation invariant elastic properties en
dc.subject automatic curve classification en
dc.subject parasite automatic identification en
dc.subject straightening deformed objects en
dc.subject image analysis en
dc.subject elastic deformation en
dc.subject pattern classification techniques en
dc.subject.classification Computer Science, Artificial Intelligence en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.other IMAGE-ANALYSIS en
dc.subject.other SEGMENTATION en
dc.title A General Methodology for the Determination of 2D Bodies Elastic Deformation Invariants: Application to the Automatic Identification of Parasites en
heal.type journalArticle en
heal.identifier.primary 10.1109/TPAMI.2009.70 en
heal.identifier.secondary http://dx.doi.org/10.1109/TPAMI.2009.70 en
heal.language English en
heal.publicationDate 2010 en
heal.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. en
heal.publisher IEEE COMPUTER SOC en
heal.journalName IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE en
dc.identifier.doi 10.1109/TPAMI.2009.70 en
dc.identifier.isi ISI:000275569300003 en
dc.identifier.volume 32 en
dc.identifier.issue 5 en
dc.identifier.spage 799 en
dc.identifier.epage 814 en


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