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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 Dimitrios, A 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 2014-03-01T01:59:35Z
dc.date.available 2014-03-01T01:59:35Z
dc.date.issued 2010 en
dc.identifier.issn 01628828 en
dc.identifier.uri http://hdl.handle.net/123456789/28997
dc.subject Automatic curve classification en
dc.subject Deformation invariant elastic properties en
dc.subject Elastic deformation en
dc.subject Image analysis en
dc.subject Parasite automatic identification en
dc.subject Pattern classification techniques en
dc.subject Straightening deformed objects en
dc.subject.other Automatic identification en
dc.subject.other Elastic properties en
dc.subject.other Parasite- en
dc.subject.other Pattern classification techniques en
dc.subject.other Automatic indexing en
dc.subject.other Automation en
dc.subject.other Elastic deformation en
dc.subject.other Electronic data interchange en
dc.subject.other Image analysis en
dc.subject.other Pattern recognition en
dc.subject.other Elasticity en
dc.subject.other algorithm en
dc.subject.other animal en
dc.subject.other article en
dc.subject.other automated pattern recognition en
dc.subject.other computer assisted diagnosis en
dc.subject.other decision support system en
dc.subject.other elastography en
dc.subject.other histology en
dc.subject.other human en
dc.subject.other methodology en
dc.subject.other parasite en
dc.subject.other physiology en
dc.subject.other Young modulus en
dc.subject.other Algorithms en
dc.subject.other Animals en
dc.subject.other Decision Support Techniques en
dc.subject.other Elastic Modulus en
dc.subject.other Elasticity Imaging Techniques en
dc.subject.other Humans en
dc.subject.other Image Interpretation, Computer-Assisted en
dc.subject.other Parasites en
dc.subject.other Pattern Recognition, Automated 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.identifier.secondary 4815258 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. © 2006 IEEE. en
heal.journalName IEEE Transactions on Pattern Analysis and Machine Intelligence en
dc.identifier.doi 10.1109/TPAMI.2009.70 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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