Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/5591
Title: A pattern recognition system for brain tumour grade prediction based on histopathological material and features extracted at different optical magnifications
Authors: Κωνσταντίνου, Χρήστος
Μανέας, Ευθύμιος
Γκλώτσος, Δημήτριος
Κωστόπουλος, Σπυρίδων
Ραβαζούλα, Παναγιώτα
Contributors: Κάβουρας, Διονύσης Α.
Item type: Conference publication
Keywords: Astrocytomas;Αστροκυτώματα;Brain cancer;Καρκίνο του εγκεφάλου;Biopsy;Βιοψία;Grade;Βαθμός;Diagnosis;Διάγνωση;Pattern recognition;Αναγνώριση προτύπων
Subjects: Medicine
Internal medicine
Ιατρική
Εσωτερική παθολογία
Issue Date: 3-Feb-2015
2012
Date of availability: 3-Feb-2015
Publisher: Νερατζής, Ηλίας
Σιανούδης, Ιωάννης
Βαλαής, Ιωάννης Γ.
Φούντος, Γεώργιος Π.
Abstract: The purpose of this study is to develop a computer-assisted diagnosis system for improving diagnostic accuracy in brain cancer classification into grades of malignancy. The clinical material comprised biopsies of patients with confirmed brain cancer. Images were digitized from the original material using a digital light microscopy imaging system (LEICA Axiostar plus coupled with a LEICA DFC 420C camera, Leica Microsystems GmbH). The digitized images were processed for the separation of nuclei from the surrounding tissue using edge detection techniques. Then, features were extracted from segmented nuclei at different optical magnifications to describe each sample-patient malignancy status. Moreover, samples were examined by an expert pathologist (P.R.), who assessed qualitative a number of crucial histological characteristics that are used by the World Health Organization as criteria for tumours’ grading. These features comprised the input to a pattern recognition system, which was designed in order to predict the risks of malignancy of each tumor. The system was structured using the Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifier alternatively. Using the leave-one-out method, the PNN resulted in 94.4% accuracy, while the SVM showed 96.3%. To assess the generalization of the system to unknown data, the external cross validation was used and gave 77.8% prediction for both classifiers. Results show that computer-assisted diagnosis offers a valuable tool providing second opinion consultancy to expert physicians, which contributes towards a better and more accurate diagnostic conclusion.
Description: Special issue: Workshop on Bio-Medical Instrumentation and related Engineering And Physical Sciences, Technological Educational Institute of Athens, 6 July 2012
Language: English
Citation: Konstandinou, C., Maneas, E., Glotsos, D., Kostopoulos, S., Ravazoula, P., et al. (2012). A pattern recognition system for brain tumour grade prediction based on histopathological material and features extracted at different optical magnifications. "e-Journal of Science & Technology". [Online] 7(3): 53-59. Available from: http://e-jst.teiath.gr/
Journal: e-Περιοδικό Επιστήμης & Τεχνολογίας
e-Journal of Science & Technology
Type of Journal: With a review process (peer review)
Access scheme: Publicly accessible
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/5591
Appears in Collections:Τόμος 07, τεύχος 3 (2012)

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