000130354 001__ 130354
000130354 005__ 20181203021402.0
000130354 037__ $$aARTICLE
000130354 245__ $$aHalton Sampling for Image Registration Based on Mutual Information
000130354 269__ $$a2008
000130354 260__ $$bSampling Publishing$$c2008
000130354 336__ $$aJournal Articles
000130354 520__ $$aMutual information is a widely used similarity measure for aligning multimodal medical images. At its core it relies on the computation of a discrete joint histogram, which itself requires image samples for its estimation. In this paper we study the influence of the sampling process. We show that quasi-random sampling based on Halton sequences outperforms methods based on regular sampling or on random sampling. Our results suggest that sampling itselfand not interpolation, as was previously believed is the source of two major problems associated with mutual information: the grid effect, whereby grid-aligning transformations are favored, and the overlap problem, whereby the similarity measure exhibits discontinuities. Both defects tend to impede the accuracy of registration; they also result in reduced robustness because of the presence of local optima. By estimating the joint histogram by quasi-random sampling, we solve both issues at the same time.
000130354 6531_ $$aHalton Sampling
000130354 700__ $$0240172$$aThévenaz, Philippe$$g115226
000130354 700__ $$0240563$$aBierlaire, Michel$$g118332
000130354 700__ $$0240182$$aUnser, Michaël$$g115227
000130354 773__ $$j7$$k2$$q141-171$$tSampling Theory in Signal and Image Processing
000130354 8564_ $$uhttp://bigwww.epfl.ch/publications/thevenaz0802.html$$zURL
000130354 909C0 $$0252123$$pTRANSP-OR$$xU11418
000130354 909C0 $$0252054$$pLIB$$xU10347
000130354 909CO $$ooai:infoscience.tind.io:130354$$particle$$pSTI$$pGLOBAL_SET$$pENAC
000130354 937__ $$aLIB-ARTICLE-2008-011
000130354 970__ $$aIJ-ThevBierUnse06/TRANSP-OR
000130354 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000130354 980__ $$aARTICLE