http://www.cnr.it/ontology/cnr/individuo/prodotto/ID209748

A computer-aided diagnosis approach for emphysema recognition in chest radiography. (Articolo in rivista)

Type

Prodotto della ricerca (Classe)
Articolo in rivista (Classe)

Label

A computer-aided diagnosis approach for emphysema recognition in chest radiography. (Articolo in rivista) (literal)

Anno

2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1016/j.medengphy.2012.03.011 (literal)

Alternative label

Coppini G, Miniati M, Monti S, Paterni M, Favilla R, Ferdeghini E (2013)
A computer-aided diagnosis approach for emphysema recognition in chest radiography.
in Medical engineering & physics; Elsevier, Oxford (Regno Unito)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Coppini G, Miniati M, Monti S, Paterni M, Favilla R, Ferdeghini E (literal)

Pagina inizio

63 (literal)

Pagina fine

73 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://www.sciencedirect.com/science/article/pii/S1350453312000598 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

35 (literal)

Rivista

Medical engineering & physics (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

11 (literal)

Note

Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

CNR IFC, Università di Firenze (literal)

Titolo

A computer-aided diagnosis approach for emphysema recognition in chest radiography. (literal)

Abstract

The purpose of this work is twofold: (i) to develop a CAD system for the assessment of emphysema by digital chest radiography and (ii) to test it against CT imaging. The system is based on the analysis of the shape of lung silhouette as imaged in standard chest examination. Postero-anterior and lateral views are processed to extract the contours of the lung fields automatically. Subsequently, the shape of lung silhouettes is described by polyline approximation and the computed feature-set processed by a neural network to estimate the probability of emphysema. Images of radiographic studies from 225 patients were collected and properly annotated to build an experimental dataset named EMPH. Each patient had undergone a standard two-views chest radiography and CT for diagnostic purposes. In addition, the images (247) from JSRT dataset were used to evaluate lung segmentation in postero-anterior view. System performances were assessed by: (i) analyzing the quality of the automatic segmentation of the lung silhouette against manual tracing and (ii) measuring the capabilities of emphysema recognition. As to step i, on JSRT dataset, we obtained overlap percentage (?) 92.7±3.3%, Dice Similarity Coefficient (DSC) 95.5±3.7% and average contour distance (ACD) 1.73±0.87 mm. On EMPH dataset we had ? = 93.1±2.9%, DSC = 96.1±3.5% and ACD = 1.62±0.92 mm, for the postero-anterior view, while we had ? = 94.5± 4.6%, DSC = 91.0±6.3% and ACD = 2.22±0.86 mm, for the lateral view. As to step ii, accuracy of emphysema recognition was 95.4%, with sensitivity and specificity 94.5% and 96.1% respectively. According to experimental results our system allows reliable and inexpensive recognition of emphysema on digital chest radiography. (literal)

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