Enhanced Gan-Based Data Augmentation for Multimodal Medical Images Registration | ||
| IJCI. International Journal of Computers and Information | ||
| Articles in Press, Accepted Manuscript, Available Online from 11 January 2025 | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijci.2025.337581.1186 | ||
| Authors | ||
| Sameh Zarif* 1; Khalid M. Amin2; Maryam George3; Mohy Hadhoud4; Marian Wagdy5 | ||
| 1Faculty of computers and information, Menofia university | ||
| 2Information technology dept., Faculty of computers and information, Menofia university | ||
| 3Information Technology Department Menofia University | ||
| 4Department of information technology, Faculty of computers and Information, Menofia University | ||
| 5Department of Information Technology, Faculty of Computers and Information | ||
| Abstract | ||
| Data augmentation is a crucial technique for enhancing the generalization of deep learning registration models, especially in the medical imaging domain, where high-quality and diverse multimodal data are often scarce. Prior multimodal registration approaches faced multiple limitations, such as intricate implementation processes and overfitting, which reduce the generalizability of the models and impact registration accuracy. These limitations underscore the need for improved methodologies to enhance the effectiveness of medical image analysis by advancing the progressive GAN framework that synthesizes high-quality multimodal medical images. In this study, we propose an approach based on Generative Adversarial Networks (GANs) to improve the quality and diversity of multimodal medical images. Our methodology includes preprocessing steps, real-time modifications during GAN training, and post-processing techniques to enhance the generated images. The results demonstrate that the proposed method outperforms traditional registration techniques, achieving a mean Dice Similarity Coefficient of 0.78, indicating a significant improvement in registration accuracy. These findings support the potential application of our approach in clinical settings, enhancing the effectiveness of medical image analysis. | ||
| Keywords | ||
| Gan Augmentation; Image Registration; Multimodal Images; Unsupervised Learning; Medical Image Analysis | ||
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