scholarly journals Morphological Study of the Floor of Maxillary Sinus : Three-dimensional Reconstruction from CT Images

1994 ◽  
Vol 48 (6) ◽  
pp. 824-837
Author(s):  
Mutsumi Kamihashi
Keyword(s):  
Maxillary Sinus ◽  
Morphological Study ◽  
Three Dimensional ◽  
Ct Images ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction

Personal-computer-based three-dimensional reconstruction and simulation of maxillary sinus

2002 ◽  
Vol 24 (6) ◽  
pp. 392-398 ◽  
Author(s):  
H.-J. Kim ◽  
H.-R. Yoon ◽  
K.-D. Kim ◽  
M.-K. Kang ◽  
H.-H. Kwak ◽  
...  
Keyword(s):  
Maxillary Sinus ◽  
Personal Computer ◽  
Three Dimensional ◽  
Three Dimensional Reconstruction ◽  
Dimensional Reconstruction ◽  
Computer Based

scholarly journals Fully Automatic Segmentation and Three-Dimensional Reconstruction of the Liver in CT Images

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
ZhenZhou Wang ◽  
Cunshan Zhang ◽  
Ticao Jiao ◽  
MingLiang Gao ◽  
Guofeng Zou
Keyword(s):  
Automatic Segmentation ◽  
Three Dimensional ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Ct Image ◽  
Three Dimensional Reconstruction ◽  
Robust Segmentation ◽  
Multiple Thresholds ◽  
Dimensional Reconstruction ◽  
Slope Difference

Automatic segmentation and three-dimensional reconstruction of the liver is important for liver disease diagnosis and surgical treatment. However, the shape of the imaged 2D liver in each CT image changes dramatically across the slices. In all slices, the imaged 2D liver is connected with other organs, and the connected organs also vary across the slices. In many slices, the intensities of the connected organs are the same with that of the liver. All these facts make automatic segmentation of the liver in the CT image an extremely difficult task. In this paper, we propose a heuristic approach to segment the liver automatically based on multiple thresholds. The thresholds are computed based on the slope difference distribution that has been proposed and verified in the previous research. Different organs in the CT image are segmented with the automatically computed thresholds, respectively. Then, different segmentation results are combined to delineate the boundary of the liver robustly. After the boundaries of the 2D liver in all the slices are identified, they are combined to form the 3D shape of the liver with a global energy minimization function. Experimental results verified the effectiveness of all the proposed image processing algorithms in automatic and robust segmentation of the liver in CT images.

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