In the recent years liver segmentation from CT scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver disease diagnosis, liver volume measurement, and 3D liver volume rendering. Methods: In this paper we report a review study about the semi-automatic and automatic liver segmentation techniques, and we describe our fully automatized method. Results: The survey reveals that automatic liver segmentation is still an open problem since various weaknesses and drawbacks of the proposed works must still be addressed. Our gray level based liver segmentation method has been developed to tackle all these problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra and inter observer variation. Conclusions: We believe that our technique outperforms those presented in the literature; nevertheless, a common test set with its gold standard traced by experts, and a generally accepted performance measure are required to demonstrate it.

Liver Segmentation from CT Scans : a Survey and a New Algorithm / P. Campadelli, E. Casiraghi, A. Esposito. - In: ARTIFICIAL INTELLIGENCE IN MEDICINE. - ISSN 0933-3657. - 45:2-3(2009), pp. 185-196. [10.1016/j.artmed.2008.07.020]

Liver Segmentation from CT Scans : a Survey and a New Algorithm

P. Campadelli;E. Casiraghi;
2009

Abstract

In the recent years liver segmentation from CT scans has gained a lot of importance in the field of medical image processing since it is the first and fundamental step of any automated technique for the automatic liver disease diagnosis, liver volume measurement, and 3D liver volume rendering. Methods: In this paper we report a review study about the semi-automatic and automatic liver segmentation techniques, and we describe our fully automatized method. Results: The survey reveals that automatic liver segmentation is still an open problem since various weaknesses and drawbacks of the proposed works must still be addressed. Our gray level based liver segmentation method has been developed to tackle all these problems; when tested on 40 patients it achieves satisfactory results, comparable to the mean intra and inter observer variation. Conclusions: We believe that our technique outperforms those presented in the literature; nevertheless, a common test set with its gold standard traced by experts, and a generally accepted performance measure are required to demonstrate it.
Automatic liver segmentation; Computed tomography images; Graph cut; Survey
Settore INF/01 - Informatica
2009
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/47664
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