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Proceedings Paper

A new iterative method for liver segmentation from perfusion CT scans
Author(s): Ahmed Draoua; Adélaïde Albouy-Kissi; Antoine Vacavant; Vincent Sauvage
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Paper Abstract

Liver cancer is the third most common cancer in the world, and the majority of patients with liver cancer will die within one year as a result of the cancer. Liver segmentation in the abdominal area is critical for diagnosis of tumor and for surgical procedures. Moreover, it is a challenging task as liver tissue has to be separated from adjacent organs and substantially the heart. In this paper we present a novel liver segmentation iterative method based on Fuzzy C-means (FCM) coupled with a fast marching segmentation and mutual information. A prerequisite for this method is the determination of slice correspondences between ground truth that is, a few images segmented by an expert, and images that contain liver and heart at the same time.

Paper Details

Date Published: 11 March 2014
PDF: 9 pages
Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90371P (11 March 2014); doi: 10.1117/12.2043576
Show Author Affiliations
Ahmed Draoua, ISIT Lab., CNRS, Univ. d'Auvergne (France)
Adélaïde Albouy-Kissi, ISIT Lab., CNRS, Univ. d'Auvergne (France)
Antoine Vacavant, ISIT Lab., CNRS, Univ. d'Auvergne (France)
Vincent Sauvage, ISIT Lab., CNRS, Univ. d'Auvergne (France)


Published in SPIE Proceedings Vol. 9037:
Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)

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