Localization is an important step in the radiation treatment planning. The use of 4DCT data can enhance the efficiency of the planning when a target region is deformed by respiratory motion. Conversely, image quality in soft tissue is low since it utilizes low energy to collect data in order to limit the accumulated dose in a patient. This paper presents a method of liver segmentation in 4DCT data including high image noise and metal artifact. The proposed method was based on a level-set method using reaction-diffusion evolution and modification of a Chan-Vese model. Automatic segmentation was independently performed on each CT volume in a breathing cycle. From our results, the global shape of the liver was extracted smoothly without detecting extraordinary regions. The displacement computed from the center of mass of the liver-segmented volume was similar to a movement trend of two metal markers placed inside the liver.
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