Reconstructing and resizing 3D images from DICOM files
Aziz Fajar, Riyanarto Sarno, Chastine Fatichah, Achmad Fahmi
Abstract
Current reconstruction of 3D images from DICOM (Digital Imaging and Communications in Medicine) files requires strict supervision for the reconstructed images to have the same metadata including slice thickness, spacing between slices, and image resolution. We propose an algorithm for reconstructing 3D images based on medical images in the DICOM format with varied metadata and resizing the 3D images while preserving the annotations. The 3D image resizing may facilitate processing because most current systems cannot handle the huge 3D image data sizes. After resizing the 3D images, the original annotations that can be used as ground truths to train and evaluate machine learning method are preserved by projection. Experimental results show that the proposed method can handle various DICOM files and correctly project annotations onto the resized image.