Technical note

Standardized and semiautomated Harris lines detection

Dados Bibliográficos

ID8311627
AutoresS Suter, Susanne K Suter (0000-0002-7998-7930, University of Zurich), Matthias Harders (0000-0001-7564-6234, ETH Zurich), Christina Papageorgopoulou (0000-0002-4145-950X, University of Zurich), Gisela Kuhn (University of Zurich), Gábor Székely (0000-0002-6560-8530, ETH Zurich), F J Rühli (0000-0001-7937-001X, University of Zurich, autor correspondente)
Ano2008
Volume137
Fascículo3
Páginas362-366
Data de publicação2008-11-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoAmerican Journal of Physical Anthropology (JOURNAL)
Identificadores do periódicoISSN: 0002-9483 • E-ISSN: 1096-8644
EditoraWiley (PUBLISHER • GB)
DOI10.1002/ajpa.20901
PMID18711729
OpenAlexW2107800327
IdiomaEN
Citações recebidas6
Referências citadas17

Arrest in long bone growth and the subsequent resumption of growth may be visible as radiopaque transverse lines in radiographs (Harris lines, HL; Harris, HA. 1933. Bone growth in health and disease. London: Oxford University Press). The assessment of individual age at occurrence of such lines, as part of paleopathological skeletal studies, is time‐consuming and shows large intra‐ and interobserver variability. Thus, a standardized, automated detection algorithm would help to increase the validity of such paleopathological research. We present an image analysis application facilitating automatic detection of HL. On the basis of established age calculation methods, the individual age‐at‐formation can be automatically assessed with the tool presented. Additional user input to confirm the automatic result is possible via an intuitive graphical user interface. Automated detection of HL from digital radiographs of a sample of late Medieval Swiss tibiae was compared to the consensus of manual assessment by two blinded expert observers. The intra‐ and interobserver variability was high. The quality of the observer result improved when standardized detection criteria were defined and applied. The newly developed algorithm detected two‐thirds of the HL that were identified as consensus lines between the observers. It was, however, necessary to validate the last one‐third by manual editing. The lack of a large test series must be noted. The application is freely available for further testing by any interested researcher. Am J Phys Anthropol, 2008. © 2008 Wiley‐Liss, Inc

Medical physics · Anomaly Detection Techniques and Applications · Computer Science · Image and Object Detection Techniques · Medicine · Remote Sensing and LiDAR Applications