A general methodology for the determination of 2D bodies elastic deformation invariants: Application to the automatic identification of parasites

Dimitrios, A; Rousopoulos, P; Papaodysseus, C; Panagopoulos, M; Loumou, P; Theodoropoulos, G

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	https://dspace.lib.ntua.gr/xmlui/handle/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