Computer image analysis of brain CT images for discriminating hypodense cerebral lesions in children

dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Πρασόπουλος, Παναγιώτης Π.	el
dc.date.accessioned	2015-04-29T08:05:08Z
dc.date.issued	2015-04-29
dc.identifier.uri	http://hdl.handle.net/11400/9229
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://informahealthcare.com/doi/abs/10.3109/14639239409044717	en
dc.subject	Computer analysis
dc.subject	Classification--Archives
dc.subject	Ανάλυση υπολογιστή
dc.subject	Ταξινόμηση
dc.title	Computer image analysis of brain CT images for discriminating hypodense cerebral lesions in children	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85026720
heal.identifier.secondary	doi: 10.3109/14639239409044717
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	1994
heal.bibliographicCitation	Cavouras, D. and Prassopoulos, P. (1994). Computer image analysis of brain CT images for discriminating hypodense cerebral lesions in children. Informatics for Health and Social Care. 19(1). pp. 13-20. Informa Healthcare: 1994.	en
heal.abstract	A computer software system was designed for the automatic discrimination of focal oedemas from local glioses in brain CT examinations. Image analysis methods were applied to the images of 77 CT examinations of children with focal oedemas (42) or local glioses (35). Textural features derived from the co-occurrence matrix of the lesion's image and a neural network classifier (the multilayer perceptron) were employed for the design of the system. Best classification accuracy (89-6%) was achieved by two textural features (contrast-difference entropy), one hidden layer and three hidden nodes of the classifier. The proposed software system provides new textural information and may be of value to the radiologist in differentiating focal oedemas from local glioses, especially in small lesions, where other radiological criteria are not evident.	en
heal.publisher	Informa Healthcare	en
heal.journalName	Informatics for Health and Social Care	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false