WSEAS Transactions on Biology and Biomedicine
Print ISSN: 1109-9518, E-ISSN: 2224-2902
Volume 22, 2025
PreProcMed: Automated Medical Image Processing Framework for Deep Learning Applications
Authors: , ,
Abstract: Deep Learning applications have recently emerged for processing and analyzing medical image
content. DICOM files are the raw data or the input data for Deep Learning (DL) models used to achieve various
tasks such as segmentation, classification, and detection in medical diagnosis. However, such files cannot be
used as specific devices produce them; they need multiple pre-processing actions due to the complexity of the
problem definition when designing a DL model for medical image research. This paper introduces the
innovative PreProcMed framework for data curation, medical image processing, and feature exploration.
PreProcMed framework, a pioneering solution, has innovative features chained in an automated workflow
using only Python technology, namely i). a new approach in data curation by automated de-identification and
anonymization with the option of ii). Selection of the required MRI sequence for the DL model, followed by
iii). automated conversion of 2D images in 3D volumes that are easy to use in segmentation models and fully
iv). integrates annotation tools like ITKSnap and visualization tools like 3D Slicer. PreProcMed is a modular
and flexible framework ready to be modified and adapted according to DL models used for medical imaging
research.
Search Articles
Keywords: Medical image processing, DICOM, NIFTI, automated workflow, Python, ANVIL, Deep
Learning Model
Pages: 181-189
DOI: 10.37394/23208.2025.22.19