| Autor: |
Samant, Sunita, Nanda, Pradipta Kumar, Ghosh, Ashish, Sahoo, Subhaluxmi, Panda, Adya Kinkar |
| Zdroj: |
International Journal of Computational Vision and Robotics; 2024, Vol. 14 Issue: 5 p540-570, 31p |
| Abstrakt: |
In this paper, a new scheme for the registration of brain CT and noisy MR images is proposed in a multi-resolution framework based on the notions of embedded entropy and nonlinear combination of the mutual information (MI) corresponding to Renyi's and Tsallis entropy. Gabor and Sobel's features are fused probabilistically and the registration is carried out in fused feature space. The weights for the fusion of the two distributions are obtained using the Bhattacharyya distance as the similarity measure. Registration parameter is obtained at different resolutions by maximising the combined mutual information obtained at different resolutions. The proposed algorithm is tested with the real patient data obtained from Retrospective Image Registration Evaluation (RIRE) database. It is found that the optimum registration parameter obtained at a low resolution of (64 × 64) has high accuracy. The proposed scheme exhibits improved performance as compared to other existing algorithms. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
|
