In external beam radiotherapy, one of the most common and reliable methods for patient geometrical setup and/or predicting the tumor location is use of external markers. In this study, the main challenging issue is in...
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In external beam radiotherapy, one of the most common and reliable methods for patient geometrical setup and/or predicting the tumor location is use of external markers. In this study, the main challenging issue is increasing the accuracy of patient setup by investigating external markers location. Since the location of each external marker may yield different patient setup accuracy, it is important to assess different locations of external markers using appropriate selective algorithms. To do this, two commercially available algorithms entitled a) canonical correlation analysis (CCA) and b) principal component analysis (PCA) were proposed as input selection algorithms. They work on the basis of maximum correlation coefficient and minimum variance between given datasets. The proposed input selection algorithms work in combination with an adaptive neuro-fuzzy inference system (ANFIS) as a correlation model to give patient positioning information as output. Our proposed algorithms provide input file of ANFIS correlation model accurately. The required dataset for this study was prepared by means of a NURBS-based 4D XCAT anthropomorphic phantom that can model the shape and structure of complex organs in human body along with motion information of dynamic organs. Moreover, a database of four real patients undergoing radiation therapy for lung cancers was utilized in this study for validation of proposed strategy. Final analyzed results demonstrate that input selection algorithms can reasonably select specific external markers from those areas of the thorax region where root mean square error (RMSE) of ANFIS model has minimum values at that given area. It is also found that the selected marker locations lie closely in those areas where surface point motion has a large amplitude and a high correlation.
This paper presents details on the implementation of evolving Takagi-Sugeno-Kang (TSK) fuzzy models of a nonlinear process represented by the pendulum dynamics in the framework of the representative pendulum-crane sys...
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ISBN:
(纸本)9789897581496
This paper presents details on the implementation of evolving Takagi-Sugeno-Kang (TSK) fuzzy models of a nonlinear process represented by the pendulum dynamics in the framework of the representative pendulum-crane systems. The pendulum angle is the output variable of the TSK fuzzy models that are obtained by online identification. The rule bases and the parameters of the TSK fuzzy models are continuously evolved by an online identification algorithm (OIA) that adds new rules with more summarization power and modifies the existing rules and parameters. The OIA is associated with an input selection algorithm that guides the modelling in terms of ranking the inputs according to their importance factors. Three TSK fuzzy models evolved by the OIA are exemplified. The performance of the new evolving TSK fuzzy models is illustrated by experimental results conducted on pendulum-crane laboratory equipment.
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