Support vector machines based analysis of brain SPECT images for determining cerebral abnormalities in asymptomatic diabetic patients

dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Παππάς, Δ.	el
dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.date.accessioned	2015-06-07T22:06:05Z
dc.date.issued	2015-06-08
dc.identifier.uri	http://hdl.handle.net/11400/15574
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/10.1080/14639230310001613449	en
dc.subject	Computer-based classification
dc.subject	Diabetes mellitus type II
dc.subject	Ταξινόμηση μέσω υπολογιστή
dc.subject	Διαβήτης τύπου ΙΙ
dc.title	Support vector machines based analysis of brain SPECT images for determining cerebral abnormalities in asymptomatic diabetic patients	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.identifier.secondary	DOI: 10.1080/14639230310001613449
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	2003
heal.bibliographicCitation	Kalatzis, I., Pappas, D., Piliouras, N. and Cavouras, D. (2003). Support vector machines based analysis of brain SPECT images for determining cerebral abnormalities in asymptomatic diabetic patients. Informatics for Health and Social Care. 28(3). pp. 221-230. Informa Plc: 2003.	en
heal.abstract	Purpose: An image processing method was developed to investigate whether brain SPECT images of patients with diabetes mellitus type II (DMII) and no brain damage differ from those of normal subjects. Materials and methods: Twenty-five DMII patients and eight healthy volunteers underwent brain 99mTc-Bicisate SPECT examination. A semi-automatic method, allowing for physician's interaction, was developed to delineate specific brain regions (ROIs) on the SPECT images. Twenty-eight features from the grey-level histogram and the spatial-dependence matrix were computed from numerous small image-samples collected from each specific ROI. Classification into ‘diabetics’ and ‘non-diabetics’ was performed for each ROI separately. The classical least squares-minimum distance (LSMD) classifier and the recently developed support vector machines (SVM) classifier were used. System performance was evaluated by means of the leave-one-out method; one sample was left out, the classifier was trained by the rest of the samples, and the left-out sample was classified. By repeating for all samples, the classifier's performance could be tested on data not incorporated in its design. Results: Highest classification accuracies (LSMD: 97.8%, SVM: 99.1%) were achieved at the right occipital lobule employing two features, the standard deviation and entropy. For the rest of the ROIs classification accuracies ranged between 84.5 and 98.6%. Conclusion: Our findings indicate cerebral blood flow disruption in patients with DMII. The proposed system may assist physicians in evaluating cerebral blood flow in patients with DMII undergoing brain SPECT.	en
heal.publisher	Informa Plc	en
heal.journalName	Informatics for Health and Social Care	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false