A digital image analysis and neural network based system for identification of third-stage parasitic strongyle larvae from domestic animals

Theodoropoulos, G; Loumos, V; Anagnostopoulos, C; Kayafas, E; Martinez-Gonzales, B

dc.contributor.author	Theodoropoulos, G	en
dc.contributor.author	Loumos, V	en
dc.contributor.author	Anagnostopoulos, C	en
dc.contributor.author	Kayafas, E	en
dc.contributor.author	Martinez-Gonzales, B	en
dc.date.accessioned	2014-03-01T01:15:25Z
dc.date.available	2014-03-01T01:15:25Z
dc.date.issued	2000	en
dc.identifier.issn	0169-2607	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13483
dc.subject	image analysis	en
dc.subject	image identification	en
dc.subject	neural network	en
dc.subject	parasite	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Biomedical	en
dc.subject.classification	Medical Informatics	en
dc.title	A digital image analysis and neural network based system for identification of third-stage parasitic strongyle larvae from domestic animals	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/S0169-2607(99)00056-5	en
heal.identifier.secondary	http://dx.doi.org/10.1016/S0169-2607(99)00056-5	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	A competitive learning vector quantization artificial neural network (ANN) was trained to identify third-stage parasitic strongyle larvae fi om domestic animals on the basis of quantitative data obtained from processed digital images of larvae, For this reason, various quantitative features obtained from processed digital images of larvae were tested as to whether they are variant or invariant to the shape taken by the motile larvae during image recording. A total of 255 images of 57 individual larvae in various shapes belonging to five genera were recorded. Following image processing, 16 features were measured, of which seven were selected as invariant to larva shape. By trial and error, two of those features, 'area' and 'perimeter', along with the quantitative features used in conventional identification, 'overall body length', 'width' and 'extension of sheath' (tip of larva to tip of sheath), were used as an effective training data set for the ANN. This ANN coupled with an image analysis facility and a knowledge relational database became the basis for developing a computer-based larva identification system whose overall identification performance was 91.9%. The advantages of this system are its speed and objectivity. The objectivity of the system is based on the fact that it is not subject to inter- and intra-observer variability arising from the user's profile of competency in interpreting subjective and non-quantifiable descriptions. The limitations of the system are that it cannot handle raw images but only data extracted from images, its performance depends on the reliability of the input vectors used as training data for the ANN, and its use is restricted only to well-equipped laboratories due to its requirement for expensive instrumentation. (C) 2000 Elsevier Science Ireland Ltd. All rights reserved.	en
heal.publisher	ELSEVIER SCI IRELAND LTD	en
heal.journalName	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE	en
dc.identifier.doi	10.1016/S0169-2607(99)00056-5	en
dc.identifier.isi	ISI:000086633100001	en
dc.identifier.volume	62	en
dc.identifier.issue	2	en
dc.identifier.spage	69	en
dc.identifier.epage	76	en