This work is a part of the MAGIC-5 (Medical Applications on a Grid Infrastructure Connection) experiment of the Italian INFN (Istituto Nazionale di Fisica Nucleare). A simple CAD (Computer-Assisted Detection) system for juxta-pleural lung nodules in CT images is presented, with the purpose of comparing different 2D concavity-patching techniques and assessing the respective efficiency in locating nodules. After a short introduction on the motivation, and a review of some CAD systems for lung nodules already published by the MAGIC-5 Collaboration, the paper describes the main lines of this particular approach, giving preliminary results and comments. In our procedure, candidate nodules are identified by patching lung border concavities in a hierarchical multiscale framework. Once located, they are fed to an artificial neural network for false positive reduction. The system has a modular structure that easily allows the insertion of arbitrary border-smoothing functions for concavity detection and nodule searching. In this paper the α-hull and morphological closing are compared, proving the higher sensitivity of the former, which also appears computationally less heavy. © 2010 Elsevier B.V. All rights reserved.

Approaches to juxta-pleural nodule detection in CT images within the MAGIC-5 Collaboration

G. De Nunzio;A. Massafra;R. Cataldo;I. De Mitri;
2011-01-01

Abstract

This work is a part of the MAGIC-5 (Medical Applications on a Grid Infrastructure Connection) experiment of the Italian INFN (Istituto Nazionale di Fisica Nucleare). A simple CAD (Computer-Assisted Detection) system for juxta-pleural lung nodules in CT images is presented, with the purpose of comparing different 2D concavity-patching techniques and assessing the respective efficiency in locating nodules. After a short introduction on the motivation, and a review of some CAD systems for lung nodules already published by the MAGIC-5 Collaboration, the paper describes the main lines of this particular approach, giving preliminary results and comments. In our procedure, candidate nodules are identified by patching lung border concavities in a hierarchical multiscale framework. Once located, they are fed to an artificial neural network for false positive reduction. The system has a modular structure that easily allows the insertion of arbitrary border-smoothing functions for concavity detection and nodule searching. In this paper the α-hull and morphological closing are compared, proving the higher sensitivity of the former, which also appears computationally less heavy. © 2010 Elsevier B.V. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/445789
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