This paper proposes a new segmentation method for the delimitation of the lung parenchyma in thorax Computed-Tomography (CT) datasets, which will be used as pre-processing step in the CAD (Computer Assisted Detection) system for lung nodule detection that is being developed by the MAGIC-5 (Medical Applications in a Grid Infrastructure Connection) Collaboration. Once finished, the CAD software will run in an integrated “grid” environment, where the potentiality of distributed resources for both data and computation will be exploited. The algorithm is fully automated and three-dimensional (3D). Its most innovative part – to the best of our knowledge – is the segmentation of the external airways (trachea and bronchi), obtained by 3D region growing with wavefront simulation and suitable stop conditions. Another original element is the technique used to check and solve the problem of the apparent ‘fusion’ between the lungs, caused by partial volume effects. A general overview of the algorithm is given, with some details of the innovative parts. The results of its application to a database of about 130 high-resolution lowdose images are discussed.

An Innovative Lung Segmentation Algorithm in CT Images with Accurate Delimitation of the Hilus Pulmonis

DE MITRI, IVAN
2008

Abstract

This paper proposes a new segmentation method for the delimitation of the lung parenchyma in thorax Computed-Tomography (CT) datasets, which will be used as pre-processing step in the CAD (Computer Assisted Detection) system for lung nodule detection that is being developed by the MAGIC-5 (Medical Applications in a Grid Infrastructure Connection) Collaboration. Once finished, the CAD software will run in an integrated “grid” environment, where the potentiality of distributed resources for both data and computation will be exploited. The algorithm is fully automated and three-dimensional (3D). Its most innovative part – to the best of our knowledge – is the segmentation of the external airways (trachea and bronchi), obtained by 3D region growing with wavefront simulation and suitable stop conditions. Another original element is the technique used to check and solve the problem of the apparent ‘fusion’ between the lungs, caused by partial volume effects. A general overview of the algorithm is given, with some details of the innovative parts. The results of its application to a database of about 130 high-resolution lowdose images are discussed.
Computer aided diagnosis; Digital imaging; Image analysis; Lung tumor; Image segmentation
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12571/1232
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