@article {218,
	title = {An adaptive Monte Carlo approach to phase-based multimodal image registration},
	journal = {IEEE Transactions on Information Technology in Biomedicine},
	volume = {14},
	year = {2010},
	pages = {173 - 179},
	abstract = {<p>In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.</p>
},
	keywords = {adaptive Monte Carlo scheme, adaptive sampling approach, Algorithms, Computer-Assisted, Diagnostic Imaging, geometric transformation, Humans, image interpretation, image phase moments, image processing, image registration, iterative methods, Least-Squares Analysis, medical image processing, Monte Carlo Method, Monte Carlo methods, multidimensional sampling distribution, multidimensional solution space, multiresolution algorithm, optimal image alignment, Pearson type VII error, phase based multimodal image registration, random solution candidate generation, sampling methods},
	issn = {1089-7771},
	doi = {http://dx.doi.org/10.1109/TITB.2009.2035693},
	author = {A Wong}
}
