A new iterative method for liver segmentation from perfusion CT scans

Ahmed Draoua; Adélaïde Albouy-Kissi; Antoine Vacavant; Vincent Sauvage

doi:10.1117/12.2043576

11 March 2014 A new iterative method for liver segmentation from perfusion CT scans

Ahmed Draoua, Adélaïde Albouy-Kissi, Antoine Vacavant, Vincent Sauvage

Proceedings Volume 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment; 90371P (2014) https://doi.org/10.1117/12.2043576
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

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.

Citation Download Citation

Ahmed Draoua, Adélaïde Albouy-Kissi, Antoine Vacavant, and Vincent Sauvage "A new iterative method for liver segmentation from perfusion CT scans", Proc. SPIE 9037, Medical Imaging 2014: Image Perception, Observer Performance, and Technology Assessment, 90371P (11 March 2014); https://doi.org/10.1117/12.2043576

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