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

Arabadjis, D; Rousopoulos, P; Papaodysseus, C; Panagopoulos, M; Loumou, P; Theodoropoulos, G

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