A new Active Contour Model (ACM) algorithm for the detection of the contour of bi-dimensional regions is presented. The algorithm is based on the simulation of an elastic band glued to the contour of the region under analysis. As a result a local convex hull is obtained, where the radius of the concave regions included by the elastic band is defined by properly tuning a parameter A dedicated application to medical images is presented. The algorithm is part of a segmentation system able to extract the lung volume from 3D CT scans. The effectiveness of the algorithm is evaluated on a database of 15 low-dose CT scans (about 320 sectional images per CT), including 26 nodules. No pathological structure is missing after the lung volume segmentation, while a reduction of the volume to analyze is obtained to about 15% of the total volume of the original CT scan, and 25% of the chest volume.
A novel Active Contour Model algorithm for contour detection in complex objects / Gargano, G.; Bellotti, R.; De Carlo, F.; Tangaro, S.; Tommasi, E.; Castellano, Marcello; Cerello, P.; Cheran, S. C.; Fulcheri, C.. - (2007), pp. 49-53. (Intervento presentato al convegno IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, CIMSA 2007 tenutosi a Ostuni, Italy nel June 27-29, 2007) [10.1109/CIMSA.2007.4362537].
A novel Active Contour Model algorithm for contour detection in complex objects
CASTELLANO, Marcello;
2007-01-01
Abstract
A new Active Contour Model (ACM) algorithm for the detection of the contour of bi-dimensional regions is presented. The algorithm is based on the simulation of an elastic band glued to the contour of the region under analysis. As a result a local convex hull is obtained, where the radius of the concave regions included by the elastic band is defined by properly tuning a parameter A dedicated application to medical images is presented. The algorithm is part of a segmentation system able to extract the lung volume from 3D CT scans. The effectiveness of the algorithm is evaluated on a database of 15 low-dose CT scans (about 320 sectional images per CT), including 26 nodules. No pathological structure is missing after the lung volume segmentation, while a reduction of the volume to analyze is obtained to about 15% of the total volume of the original CT scan, and 25% of the chest volume.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
