• Communications for Statistical Applications and Methods
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• v.19 no.6
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• pp.885-898
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• 2012

In this paper, we consider Bayesian approaches to partially linear models, in which a regression function is represented by a semiparametric additive form of a parametric linear regression function and a nonparametric regression function. We make a comparative study on the performance of widely used Bayesian partially linear models in terms of empirical analysis. Specifically, we deal with three Bayesian methods to estimate the nonparametric regression function, one method using Fourier series representation, the other method based on Gaussian process regression approach, and the third method based on the smoothness of the function and differencing. We compare the numerical performance of three methods by the root mean squared error(RMSE). For empirical analysis, we consider synthetic data with simulation studies and real data application by fitting each of them with three Bayesian methods and comparing the RMSEs.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.6
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• pp.490-496
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• 2014

The thickness of knee joint cartilage causes most diseases of knee. Therefore, an articular cartilage segmentation of knee magnetic resonance imaging (MRI) is required to diagnose a knee diagnosis correctly. In particular, fully automatic segmentation method of knee joint cartilage enables an effective diagnosis of knee disease. In this paper, we analyze a well-known level-set based segmentation method in brain MRI, and apply that method to knee MRI with solving some problems from different image characteristics. The proposed method, a fully automatic segmentation in whole process, enables to process faster than previous semi-automatic segmentation methods. Also it can make a three-dimension visualization which provides a specialist with an assistance for the diagnosis of knee disease. In addition, the proposed method provides more accurate results than the existing methods of articular cartilage segmentation in knee MRI through experiments.