n the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. Some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D volume rendering of the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart, bones (i.e. ribs and spine), liver and its blood vessels, kidneys, and spleen. The overall system has been evaluated on the data of 100 patients, obtaining a good assessment both by visual inspection by three experts, and by comparing the computed results to the boundaries manually traced by experts.

Automatic abdominal organ segmentation from CT images / E. Casiraghi, P. Campadelli, S. Pratissoli, G. Lombardi. - In: ELCVIA. ELECTRONIC LETTERS ON COMPUTER VISION AND IMAGE ANALYSIS. - ISSN 1577-5097. - 8:1(2009 Jul). [10.5565/rev/elcvia.206]

Automatic abdominal organ segmentation from CT images

E. Casiraghi;P. Campadelli;S. Pratissoli;G. Lombardi
2009

Abstract

n the recent years a great deal of research work has been devoted to the development of semi-automatic and automatic techniques for the analysis of abdominal CT images. Some of the current interests are the automatic diagnosis of liver, spleen, and kidney pathologies and the 3D volume rendering of the abdominal organs. The first and fundamental step in all these studies is the automatic organs segmentation, that is still an open problem. In this paper we propose our fully automatic system that employs a hierarchical gray level based framework to segment heart, bones (i.e. ribs and spine), liver and its blood vessels, kidneys, and spleen. The overall system has been evaluated on the data of 100 patients, obtaining a good assessment both by visual inspection by three experts, and by comparing the computed results to the boundaries manually traced by experts.
Abdominal CT images ; 3D organs segmentation ; histogram analysis ; graph cut ; α-expansion ; features and image descriptors ; classification and clustering ; colour and texture ; medical diagnosis
Settore INF/01 - Informatica
lug-2009
http://elcvia.cvc.uab.es/article/view/206/209
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/237248
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