Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas
Φόρτωση...
Ημερομηνία
Συγγραφείς
Spyridonos, P.
Ravazoula, P.
Cavouras, D.
Berberidis, K.
Nikiforidis, G.
Τίτλος Εφημερίδας
Περιοδικό ISSN
Τίτλος τόμου
Εκδότης
Περίληψη
Τύπος
Είδος δημοσίευσης σε συνέδριο
Είδος περιοδικού
peer-reviewed
Είδος εκπαιδευτικού υλικού
Όνομα συνεδρίου
Όνομα περιοδικού
Med Inform Internet Med
Όνομα βιβλίου
Σειρά βιβλίου
Έκδοση βιβλίου
Συμπληρωματικός/δευτερεύων τίτλος
Περιγραφή
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.
Περιγραφή
Λέξεις-κλειδιά
Algorithms, Eosine Yellowish-(YS)/*diagnostic use, Hematoxylin/*diagnostic use, Humans, *Image Processing, Computer-Assisted, Multivariate Analysis, Staining and Labeling/*methods, United States, Urinary Bladder Neoplasms/classification/*pathology
Θεματική κατηγορία
Παραπομπή
Σύνδεσμος
http://www.ncbi.nlm.nih.gov/pubmed/11706928
Γλώσσα
en
Εκδίδον τμήμα/τομέας
Όνομα επιβλέποντος
Εξεταστική επιτροπή
Γενική Περιγραφή / Σχόλια
Ίδρυμα και Σχολή/Τμήμα του υποβάλλοντος
Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής