Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Papp, L; Juhasz, R; Travar, S; Kolli, A; Sorantin, E.
Automatic detection and characterization of funnel chest based on spiral CT.
J Xray Sci Technol. 2010; 18(2): 137-144.
Doi: 10.3233/XST-2010-0249
Web of Science
PubMed
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- Co-Autor*innen der Med Uni Graz
- Kolli Alexander
- Sorantin Erich
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- Abstract:
- Funnel chest (Pectus excavatum) is the most common deformity of the anterior chest in children. Present paper describes a method to process and classify CT slices representing funnel chest deformities. A manually chosen CT slice was processed to detect the inner curvature of the chest for characterization. Normalized data from the detected inner curvature was gained and saved next to a manually-given deformity type for further classification rule determinations. Based on the multiple correlations of the values gained from the inner curvature, a hierarchical classification was performed on 199 patient data. Results have shown that the calculated values gained from the inner curvature can accurately characterize the deformity type of the chest. Since minimal user interaction was necessary to detect and characterize the inner curvature, our method is considered to be an effective automated procedure for funnel chest deformity classifications.
- Find related publications in this database (using NLM MeSH Indexing)
- Adolescent -
- Algorithms -
- Child -
- Female -
- Funnel Chest - radiography
- Humans -
- Image Interpretation, Computer-Assisted - methods
- Image Processing, Computer-Assisted - methods
- Male -
- Tomography, Spiral Computed - methods
- Young Adult -