• Communications for Statistical Applications and Methods
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• v.16 no.2
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• pp.277-286
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• 2009

Image registration is a process to establish the spatial correspondence between images of the same scene, which are acquired at different view points, at different times, or by different sensors. In this paper, we introduce a robust brain registration technique for correcting the difference between two temporal images by the different coordinate systems in MR and CT image obtained from the same patient. Two images are registered where this measure is minimized using a modified conditional entropy(MCE: Modified Conditional Entropy) computed from the joint histograms for the intensities of two given images, we conduct the rendering for visualization of 3D volume image.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.303-308
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• 2010

In this paper, we propose a medical image registration technique combining the gradient vector flow and modified conditional entropy. The registration is conducted by the use of a measure based on the entropy of conditional probabilities. To achieve the registration, we first define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. In order to combine the spatial information into a traditional registration measure, we use the gradient vector flow field. Then the MCE is computed from the gradient vector flow intensity (GVFI) combining the gradient information and their intensity values of original images. To evaluate the performance of the proposed registration method, we conduct experiments with our method as well as existing method based on the mutual information (MI) criteria. We evaluate the precision of MI- and MCE-based measurements by comparing the registration obtained from MR images and transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.6
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• pp.1-6
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• 2009

We propose an intensity-based image registration method for medical images. The proposed registration is performed by the use of a new measure based on the entropy of conditional probabilities. To achieve the registration, we define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. And we conduct experiments with our method as well as existing methods based on the sum of squared differences (SSD), normalized correlation coefficient (NCC), normalized mutual information (NMI) criteria. We evaluate the precision of SSD-, NCC-, MI- and MCE-based measurements by comparing the registration obtained from the same modality magnetic resonance (MR) images and the different modality transformed MR/transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.