Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
Support Vector Machine Classification using Correlation Prototypes for Bone Age Assessment
verfasst von : Markus Harmsen, Benedikt Fischer, Hauke Schramm, Thomas M. Deserno
Erschienen in: Bildverarbeitung für die Medizin 2012
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Bone age assessment (BAA) on hand radiographs is a frequent and time consuming task in radiology. Our method for automatic BAA is done in several steps: (i) extract of 14 epiphyseal regions from the radiographs, (ii) for each region, retain image features using the IRMA framework, (iii) use these features to build a classifier model, (iv) classify unknown hand images. In this paper, we combine a support vector machine (SVM) with cross-correlation to a prototype image for each class. These prototypes are obtained choosing the most similar image in each class according to mean cross-correlation. Comparing SVM with k nearest neighbor (kNN) classification, a systematic evaluation is presented using 1,097 images of 30 diagnostic classes. Mean error in age prediction is reduced from 1.0 to 0.9 years for 5-NN and SVM, respectively.