WE-AB-207B-03: A Computational Methodology for Determination of CTV-To-PTV Margins with Inter Fractional Shape Variations Based On a Statistical Point Distribution Model for Prostate Cancer Radiation Therapy

2016 ◽  
Vol 43 (6Part39) ◽  
pp. 3804-3804
Author(s):  
Y Shibayama ◽  
H Arimura ◽  
T Hirose ◽  
K Nakamura ◽  
T Sasaki ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Distribution Model ◽  
Statistical Point ◽  
Point Distribution ◽  
Computational Methodology ◽  
Point Distribution Model

Investigation of interfractional shape variations based on statistical point distribution model for prostate cancer radiation therapy

2017 ◽  
Vol 44 (5) ◽  
pp. 1837-1845 ◽  
Author(s):  
Yusuke Shibayama ◽  
Hidetaka Arimura ◽  
Taka-aki Hirose ◽  
Takahiro Nakamoto ◽  
Tomonari Sasaki ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Distribution Model ◽  
Statistical Point ◽  
Point Distribution ◽  
Point Distribution Model

scholarly journals A Multiscale Constraints Method Localization of 3D Facial Feature Points

2015 ◽  
Vol 2015 ◽  
pp. 1-6
Author(s):  
Hong-an Li ◽  
Yongxin Zhang ◽  
Zhanli Li ◽  
Huilin Li
Keyword(s):  
Facial Feature ◽  
Feature Point ◽  
Distribution Model ◽  
Feature Points ◽  
Widespread Application ◽  
Point Distribution ◽  
Localization Accuracy ◽  
Localization Method ◽  
Relative Angle ◽  
Point Distribution Model

It is an important task to locate facial feature points due to the widespread application of 3D human face models in medical fields. In this paper, we propose a 3D facial feature point localization method that combines the relative angle histograms with multiscale constraints. Firstly, the relative angle histogram of each vertex in a 3D point distribution model is calculated; then the cluster set of the facial feature points is determined using the cluster algorithm. Finally, the feature points are located precisely according to multiscale integral features. The experimental results show that the feature point localization accuracy of this algorithm is better than that of the localization method using the relative angle histograms.

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