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Content-based access to oral and maxillofacial radiographs

Journal article
Authors TM Deserno
B Molander
MO Güld
C Thies
Hans-Göran Gröndahl
Published in Dentomaxillofac Radiol
Volume 36
Issue 6
Pages 328-335
Publication year 2007
Published at Institute of Odontology
Pages 328-335
Language en
Links dmfr.birjournals.org/cgi/reprint/36...
Keywords image processing; computer-assisted;; medical informatics;; radiography; dental; digital;; information storage and retrieval
Subject categories Radiological research

Abstract

Objectives: Content-based access (CBA) to medical image archives, i.e. data retrieval by means of image-based numerical features computed automatically, has capabilities to improve diagnostics, research and education. In this study, the applicability of CBA methods in dentomaxillofacial radiology is evaluated. Methods: Recent research has discovered numerical features that were successfully applied for an automatic categorization of radiographs. In our experiments, oral and maxillofacial radiographs were obtained from the day-to-day routine of a university hospital and labelled by an experienced dental radiologist regarding the technique and direction of imaging, as well as the displayed anatomy and biosystem. In total, 2000 radiographs of 71 classes with at least 10 samples per class were analysed. A combination of co-occurrence-based texture features and correlation-based similarity measures was used in leaving-one-out experiments for automatic classification. The impact of automatic detection and separation of multi-field images and automatic separability of biosystems were analysed. Results: Automatic categorization yielded error rates of 23.20%, 7.95% and 4.40% with respect to a correct match within the first, fifth and tenth best returns. These figures improved to 23.05%, 7.00%, 4.20%, and 20.05%, 5.65% and 3.25% if automatic decomposition was applied and the classifier was optimized to the dentomaxillofacial imagery, respectively. The dentulous and implant systems were difficult to distinguish. Experiments on non-dental radiographs (10 000 images of 57 classes) yielded 12.6%, 5.6% and 3.6%. Conclusion: Using the same numerical features as in medical radiology, oral and maxillofacial radiographs can be reliably indexed by global texture features for CBA and data mining.

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