Medical image registration using the modified conditional entropy measure combining the spatial and intensity information

Myung-Eun Lee; Soo-hyung Kim; Wan-Hyun Cho; Sun-Worl Kim; Jong-Hyun Park; Soon-Young Park; Jun-Sik Lim

doi:10.1117/12.844601

12 March 2010 Medical image registration using the modified conditional entropy measure combining the spatial and intensity information

Myung-Eun Lee, Soo-hyung Kim, Wan-Hyun Cho, Sun-Worl Kim, Jong-Hyun Park, Soon-Young Park, Jun-Sik Lim

Author Affiliations +

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76233A (2010) https://doi.org/10.1117/12.844601
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

We propose an image registration technique using spatial and intensity information. 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 various 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 our proposed method is a more accurate technique.

Citation Download Citation

Myung-Eun Lee, Soo-hyung Kim, Wan-Hyun Cho, Sun-Worl Kim, Jong-Hyun Park, Soon-Young Park, and Jun-Sik Lim "Medical image registration using the modified conditional entropy measure combining the spatial and intensity information", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233A (12 March 2010); https://doi.org/10.1117/12.844601

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