Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas

dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Βερβερίδης, Κ.	el
dc.contributor.author	Νικηφορίδης, Γεώργιος Χ.	el
dc.date.accessioned	2015-05-08T09:17:02Z
dc.date.issued	2015-05-08
dc.identifier.uri	http://hdl.handle.net/11400/9930
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/10.1080/14639230110065757	en
dc.subject	Classification grading of bladder
dc.subject	Carcinoma
dc.subject	Βαθμολογική κατάταξη ουροδόχου κύστης
dc.subject	Καρκίνωμα
dc.title	Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas	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:10.1080/14639230110065757
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	2001
heal.bibliographicCitation	Spyridonos, P., Ravazoula, P., Cavouras, D., Berberidis, K. and Nikiforidis. G. (2001). Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas. Informatics for Health and Social Care. 26(3). pp. 179-190. Informa Healthcare : 2001.	en
heal.abstract	Purpose : A computer-based image analysis system was developed for assessing the malignancy of urinary bladder carcinomas in a more objective manner. Tumours characterized in accordance with the WHO grading system were classified into low-risk (grades I and II) and high-risk (grades III and IV). Materials and methods : Images from 92 haematoxylin-eosin stained sections of urinary bladder carcinomas were digitized and analysed. An adequate number of nuclei were segmented from each image for morphologic and textural analysis. Image segmentation was performed by an efficient algorithm, which used pattern recognition methods to automatically characterize image pixels as nucleus or background. Image classification into low-risk or high-risk tumours was performed by means of the quadratic non-linear Bayesian classifier, which was designed employing 36 textural and morphological features of the nucleus. Results : Automatic segmentation of nuclei on all images was about 90% on average. Overall system accuracy in correctly classifying tumours into low-risk or high-risk was 88%, employing the leave-one-out method and the best combination of three textural and one morphological feature. Classification accuracy for low-risk tumours was 88.8% and for high-risk tumours 86.2%. Conclusion : The proposed image analysis system may be of value to the objective assessment of the malignancy of urine bladder carcinomas, since it relies on nuclear parameters that are employed in visual grading and their prognostic value has been proved.	en
heal.publisher	Informa Healthcare	en
heal.journalName	Informatics for Health and Social Care	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true