29 April 2005 Adaptive reduction of intensity levels in 3D images for mutual information-based registration
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Abstract
Mutual information is currently one of the most widely used image similarity measures for multimodality image registration. An important step in the calculation of the mutual information of two images is the estimation of their joint histogram. Most algorithms use lateral joint histogram sizes that are smaller than the actual number of intensity levels present in the images being registered. Using a reduced joint histogram size is especially useful when registering small portions of the images to obtain local deformations in nonrigid registration algorithms, and when implementing hardware solutions for acceleration of mutual information calculation. The most commonly used method for reducing the size of the joint histogram is to perform a linear rescaling of intensity values. The main problem with this method is that image regions with similar intensity values but corresponding to distinct tissue types tend to merge, thus compromising the accuracy of registration. We present new algorithms for reducing the number of gray levels present in 3D medical images, and compare their performance with previously reported ones. The tested algorithms are classified in three categories: histogram shape preserving algorithms, entropy maximization algorithms and quantization error minimization algorithms. Results show that in CT and MRI registration the best accuracy is achieved using entropy maximization algorithms, while in PET and MRI registration the best accuracy is achieved using histogram shape preservation algorithms.
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Carlos Raul Castro-Pareja, Raj Shekhar, "Adaptive reduction of intensity levels in 3D images for mutual information-based registration", Proc. SPIE 5747, Medical Imaging 2005: Image Processing, (29 April 2005); doi: 10.1117/12.595499; https://doi.org/10.1117/12.595499
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