Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloComputer-aided diagnosis of emphysema in COPD patients: Neural-network-based analysis of lung shape in digital chest radiographs
Anno di pubblicazione2007
Formato
  • Elettronico
  • Cartaceo
Autore/iCoppini G.; Miniati M.; Paterni M.; Monti S.; Ferdeghini E. M.
Affiliazioni autoriCNR-Istituto di Fisiologia Clinica
Autori CNR e affiliazioni
  • EZIO MARIA FERDEGHINI
  • SIMONETTA MONTI
  • MASSIMO MINIATI
  • GIUSEPPE COPPINI
  • MARCO PATERNI
Lingua/e
  • inglese
AbstractSeveral abnormalities of the shape of lung fields (depression and flattening of the diaphragmatic contours, increased retrosternal space) are indicative of emphysema and can be accurately imaged by digital chest radiography. In this work, we aimed at developing computational descriptors of the shape of the lung silhouette able to capture the alterations associated with emphysema. We analyzed two-sided digital chest radiographs from a sample of 160 patients with chronic obstructive pulmonary disease (COPD), 60 of which were affected by emphysema, and from 160 subjects with normal lung function. Two different description schemes were considered: a first one based on lung-silhouette curvature features, and a second one based on a minimal-polyline approximation of the lung shape. Both descriptors were employed to recognize alterations of the lung shape using classifiers based on multilayer neural networks of the feed-forward type. Results indicate that pulmonary emphysema can be reliably diagnosed or excluded by using digital chest radiographs and a proper computational aid. Two-sided chest radiographs provide more accurate discrimination than single-view analysis. The minimal-polyline approximation provided significantly better results than those obtained from curvature-based features. Emphysema was detected, in the entire dataset, with an accuracy of about 90% (sensitivity 88%, specificity 90%) by using the minimal-polyline approximation.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da76
Pagine a86
Pagine totali-
RivistaMedical engineering & physics
Attiva dal 1994
Editore: Butterworth-Heinemann, - Oxford
Paese di pubblicazione: Regno Unito
Lingua: inglese
ISSN: 1350-4533
Titolo chiave: Medical engineering & physics
Titolo proprio: Medical engineering & physics.
Titolo abbreviato: Med. eng. phys.
Titolo alternativo: Medical engineering and physics
Numero volume della rivista28
Fascicolo della rivista-
DOI10.1016/j.medengphy.2006.02.001
Verificato da refereeSì: Internazionale
Stato della pubblicazione-
Indicizzazione (in banche dati controllate)
  • ISI Web of Science (WOS) (Codice:000243681400010)
Parole chiaveNeural networks, artificial life and related topics, Pulmonary Disease, Chronic Obstructive, COPD
Link (URL, URI)-
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • IFC — Istituto di fisiologia clinica
Moduli/Attività/Sottoprogetti CNR
  • ME.P06.008.001 : Tecnologie Biomediche
Progetti Europei-
Allegati
Articolo pubblicato (documento privato )
Tipo documento: application/pdf

Dati storici
I dati storici non sono modificabili, sono stati ereditati da altri sistemi (es. Gestione Istituti, PUMA, ...) e hanno solo valore storico.
Area disciplinareResearch/Laboratory Medicine & Medical Technology
Rivista ISIMEDICAL ENGINEERING & PHYSICS [10613J0]
NoteIn: Medical Engineering & Physics, vol. 28 (2) pp. 99-. The Institute of Physics and Engineering in Medicine (ed.). Elsevier Inc., 2006.