A Novel 3D Reconstruction Method for Hepatic Tumor Visualization

Haiming Ai, Shuicai Wu, Harbin Ai, Hongjian Gao, Chunlan Yang, and Yi Zeng

Keywords

Graphic theory, Image autosegmentation, New Marching Cubes, MITK

Abstract

3D shape reconstruction of the hepatic tumor from its 2D cross-sections improves the surgeon’s knowledge of tumor anatomy and makes even more complicated tumor surgery safe. Image segmentation and 3D reconstruction are two key technologies for tumor visualization. Being aimed at the difficult problem about how to effectively segment hepatic tumor from CT images, the graph-theory segmentation algorithm is first attempted to automatically extract tumor. In addition, a novel 3D reconstruction method, which effectively combines New Marching Cubes algorithm with graph-theory auto-segmentation algorithm, is proposed and employed to visualize tumor surface. The proposed method has been evaluated via CT image series of 6 patients suffering from liver cancer. The results demonstrate that segmentation accuracy is very high where the average minimum Euclidean distance and area overlap measurement are 0.101 and 0.965, 3D reconstruction is fast and the tumor model is also relatively satisfactory, and it can be employed to aid clinical practice as an alternative tool.

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