Abstract:
Medical imaging plays a vital part in modern-day healthcare practice, enabling diagnosis,
treatment planning, and follow-up in the patient. Among various imaging modalities, X-ray
imaging remains one of the most common due to its effectiveness, widespread availability,
and diagnostic relevance. However, the continuous increase in the volume and resolution of
medical images, especially X-rays, brings data storage, transmission, and processing
challenges of utmost importance. These challenges normally lead to costly and complex
management issues, especially in large healthcare systems.
To address these challenges, both traditional compression techniques and more recent
machine learning-based techniques have been explored. Traditional techniques, such as JPEG
and PNG, offer basic compression but are not tailored to the specific needs of medical
imaging since they are unable to preserve diagnostic quality or exploit the unique structural
patterns in medical images. Yet, deep learning techniques, particularly autoencoders,
convolutional neural networks (CNNs), and Variational autoencoders (VAEs), have
demonstrated excellent potential in improving both image quality and compression ratios,
surmounting the limitations of conventional techniques.
This research investigates the potential of deep learning-based image compression
models—like Autoencoders (AE), Deep Convolutional Autoencoders (DCAE), CNNs, and
VAEs—for achieving better medical X-ray image compression. It also incorporates machine
learning algorithms such as Principal Component Analysis (PCA) and K-means clustering for
the optimization of image storage and transmission. Additionally, this study introduces the
Medical X-ray Imaging Dataset (MXID), a high-quality dataset of X-ray images from
AOUINET Hospital in Tebessa, Algeria, to facilitate various medical imaging tasks, including
compression, classification, and machine learning applications. The study's findings
demonstrate that deep learning-based models, specifically DCAEs, outperform alternative
compression techniques in terms of image compression efficiency and quality retention
yielding a PSNR of 46,78 dB.
The ultimate objective of this research in the long term is to contribute to the development of
efficient, high-quality medical image compression techniques that will be able to improve the
handling of healthcare data, thereby facilitating more effective healthcare delivery through
better storage and transmission.
