Abdominal CT images have been widely studied in the recent years as they are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, that is still an open problem. In this paper we describe two automatic methods to segment the liver from abdominal CT data. They have been evaluated on the data of 40 patients, by comparing the automatically detected liver volumes to the liver boundaries manually traced by three experts. The best performing method achieves a symmetric volume difference of 95%, which is comparable to the inter and intra-personal variability of the manual segmentation produced by experts.

Automatic liver segmentation from abdominal CT scans / E. Casiraghi, P. Campadelli, G. Lombardi - In: Proceedings [of the] international conference on image analysis and processing : Modena, Italy, September 10-24, 2007Los Alamitos : IEEE Computer Society, 2007. - ISBN 0769528775. - pp. 731-736 (( Intervento presentato al 14. convegno International Conference on Image Analysis and Processing (ICIAP 2007) tenutosi a Modena nel 2007.

Automatic liver segmentation from abdominal CT scans

E. Casiraghi;P. Campadelli;G. Lombardi
2007

Abstract

Abdominal CT images have been widely studied in the recent years as they are becoming an invaluable mean for abdominal organ investigation. In the field of medical image processing, some of the current interests are the automatic diagnosis of liver pathologies and its 3D volume rendering. The first and fundamental step in all these studies is the automatic liver segmentation, that is still an open problem. In this paper we describe two automatic methods to segment the liver from abdominal CT data. They have been evaluated on the data of 40 patients, by comparing the automatically detected liver volumes to the liver boundaries manually traced by three experts. The best performing method achieves a symmetric volume difference of 95%, which is comparable to the inter and intra-personal variability of the manual segmentation produced by experts.
PROBABILISTIC ATLAS ; IMAGE SEGMENTATION ; GRAPH CUTS ; TRANSPLANTATION ; VISUALIZATION ; CONSTRUCTION
Settore INF/01 - Informatica
2007
Institute of Electrical and Electronic Engineers (IEEE)
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/35347
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