In this paper we describe a method processing Postero Anterior chest radiographs to extract a set of nodule candidate regions characterized by a low cardinality and a high sensitivity ratio. It is based on two consecutive multiscale procedures to ﬁrst enhance the visibility of the nodules and then extract a ﬁrst set of candidates. To reduce its cardinality two diﬀerent methods, applied to a set of most representative features, are described and compared: a rule based system and a feed-forward neural network trained by back-propagation. Both the systems had similarly good results. The method has been developed and tested on a standard database containing 154 radiographs of patients with lung nodules and 93 with no nodules. The ﬁnal set of candidates detected contains about 10000 regions, approximately 40 per image, and 8 true positives out of 154 are lost; the sensitivity ratio of the system is then equal to 0.95%.
A nodule detection system for postero-anterior chest radiographs / P. Campadelli, E. Casiraghi - In: Modelling, computation and optimization in information systems and management sciences : MCO 2004 : july 1-3, 2004 : proceedings / L.T.H. An, P.D. Tao. - London : Hermes Science, 2004 Jul. - ISBN 1903398215. (( Intervento presentato al 5. convegno Modelling, Computation and Optimization in Information Systems and Management Sciences(MCO) tenutosi a Metz, France nel 2004.
|Titolo:||A nodule detection system for postero-anterior chest radiographs|
|Parole Chiave:||CAD systems ; Multiscale image enhancement ; Pattern classification|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||lug-2004|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|