Imaging and Radiation Research

Signal Analysis, Image Coding, and Processing

Submission deadline: 2025-02-17
Section Editors

Section Collection Information

Dear colleagues,

Signal analysis, image coding, and processing encompass a range of techniques vital in extracting

meaningful information from various forms of data, particularly in fields like medical imaging and

computer vision. In healthcare, biomedical signal analysis with machine intelligence is an

interdisciplinary field that combines the principles of biomedical signal processing and machine

learning to extract meaningful information from various physiological signals generated by the human

body. These signals can include data from sources like electrocardiography (ECG or EKG),

electromyography (EMG), electroencephalography (EEG), medical imaging (e.g., MRI, CT scans),

and more. The primary goal of biomedical signal analysis with machine intelligence is to assist

healthcare professionals in diagnosing diseases, monitoring patient health, and making informed

medical decisions, while also facilitating real-time monitoring and feedback, particularly in critical

care settings.

Complementing signal analysis, image coding is fundamental in medical imaging modalities like

computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. Efficient image

compression techniques ensure that medical images can be stored and transmitted without sacrificing

diagnostic quality, enabling quick access to patient data and facilitating remote consultation between

healthcare professionals, especially in telemedicine applications. Meanwhile, image processing

enhances medical images by refining quality, eliminating noise, and extracting critical information.

These enhancements empower radiologists to detect and characterize abnormalities, such as tumors

and fractures, contributing to precise diagnosis and treatment planning. Additionally, image processing

aids in medical image registration, aligning images from diverse modalities for comprehensive


Recent advancements in artificial intelligence (AI) and deep learning have further revolutionized

signal analysis and image processing in healthcare. AI algorithms can analyze vast amounts of medical

data with unprecedented accuracy, leading to improved diagnostic accuracy and personalized treatment

plans. However, challenges such as data privacy concerns, interoperability issues, and the need for

standardized protocols remain.


Signal analysis , Image processing , Medical imaging,Machine intelligence ,Biomedical signals , Feature extraction ,Telemedicine , Artificial intelligence ,Classification ,Segmentation