An Efficient System for 3D Reconstruction, Segmentation, and Visualization of DICOM Medical Images for Virtual and Augmented Reality Environments

الملخص

This study used a scalable approach to convert DICOM medical files into interactive 3D models. Suitable for advanced visualization and extended reality applications. To extract metadata, we use a pydicom library; we also used the NumPy and PyVista libraries for creating images. The suggested solution uses genuine DICOM datasets that were collected from Al-Ghaydah Central Hospital. CT and MRI slices are put together into a volumetric representation after being preprocessed. Then, the Marching Cubes technique is used to recover the surface. Then, connectivity-based mesh segmentation is used to make three-dimensional visualization accurate and interactive. As a preprocessing step, the contrast and sharpness filters showed better visibility of the structures, and the segmentation provided well-recognized anatomical separation. Reconstruction times were held within reasonable limits (1-5 seconds, depending on the size and quality of the dataset), and GUI responsiveness was also maintained throughout the entire procedure. The system's ability to produce complex models from real clinical data underscores its value in medical education, simulation, and research.