IEEE Transactions on Knowledge and Data Engineering, 22(6):900-905, June 2010 (article)
Clustering methods utilizing support estimates of a data distribution have recently attracted much attention because of their ability to generate cluster boundaries of arbitrary shape and to deal with outliers efficiently. In this paper, we propose a novel dissimilarity measure based on a dynamical system associated with support estimating functions. Theoretical foundations of the proposed measure are developed and applied to construct a clustering method that can effectively partition the whole data space. Simulation results demonstrate that clustering based on the proposed dissimilarity measure is robust to the choice of kernel parameters and able to control the number of clusters efficiently.
In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages: 186-193, IEEE Service Center, Piscataway, NJ, USA, CVPR, June 2009 (inproceedings)
Multi-modal image registration is a challenging problem
in medical imaging. The goal is to align anatomically
identical structures; however, their appearance in images
acquired with different imaging devices, such as CT
or MR, may be very different. Registration algorithms generally
deform one image, the floating image, such that it
matches with a second, the reference image, by maximizing
some similarity score between the deformed and the reference
image. Instead of using a universal, but a priori fixed
similarity criterion such as mutual information, we propose
learning a similarity measure in a discriminative manner
such that the reference and correctly deformed floating
images receive high similarity scores. To this end, we
develop an algorithm derived from max-margin structured
output learning, and employ the learned similarity measure
within a standard rigid registration algorithm. Compared
to other approaches, our method adapts to the specific registration
problem at hand and exploits correlations between
neighboring pixels in the reference and the floating image.
Empirical evaluation on CT-MR/PET-MR rigid registration
tasks demonstrates that our approach yields robust performance
and outperforms the state of the art methods for
multi-modal medical image registration.
IEEE Transactions on Neural Networks, 20(4):721-729, April 2009 (article)
In this brief, a novel method that constructs a sparse kernel machine is proposed. The proposed method generates attractors as sparse solutions from a built-in kernel machine via a dynamical system framework. By readjusting the corresponding coefficients and bias terms, a sparse kernel machine that approximates a conventional kernel machine is constructed. The simulation results show that the constructed sparse kernel machine improves the efficiency of testing phase while maintaining comparable test error.
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