Medical Image Analysis - Elsevier - Impact Factor

Medical Image Analysis - Elsevier | 2024 Impact Factor:11.8 | Cite Score:26.6 | Q1

Medical Image Analysis Journal - Elsevier

Impact Factor and Journal Rank of Medical Image Analysis

About: Medical Image Analysis is a leading international journal dedicated to the advancement of image processing and analysis techniques specifically tailored for medical imaging applications. Published by Elsevier, the journal focuses on providing a platform for high-quality research that contributes to the development, validation, and application of novel methodologies in medical image analysis.
Content: Medical Image Analysis publishes original research articles, review papers, and technical notes that present significant advances in the methodology and application of image analysis techniques in medicine. The journal also features special issues focused on emerging topics and challenges in the field.
Audience: The journal is targeted at a diverse audience, including researchers, academics, clinicians, and professionals working in the fields of medical imaging, computer vision, radiology, biomedical engineering, and related disciplines. Its content is designed to be accessible to both specialists in image analysis and healthcare practitioners interested in the application of these technologies.
Interdisciplinary Approach: The journal emphasizes an interdisciplinary approach, encouraging collaboration between experts in medical imaging, computer science, engineering, and clinical practice. This fosters the development of comprehensive solutions that address complex challenges in medical image analysis and improve patient care.
High Standards and Impact: Medical Image Analysis is recognized for its high standards of quality and rigor. Articles undergo a thorough peer-review process to ensure the publication of innovative, scientifically sound, and impactful research. The journals contributions are highly influential, often cited in academic research and clinical practice.
Global Reach: As an international journal, "Medical Image Analysis" attracts contributions from researchers and institutions worldwide, facilitating the exchange of ideas and advancements across different geographical regions and healthcare systems. This global perspective enhances the relevance and applicability of the research published.
Significance: Medical Image Analysis" is a leading journal dedicated to the advancement of image processing and analysis techniques for medical imaging applications. Through its high-quality content, interdisciplinary approach, and global reach, the journal serves as a valuable resource for researchers, clinicians, and professionals aiming to improve medical imaging technologies and enhance patient care.

Journal Home: Journal Homepage

Editor-in-Chief: Nicholas Ayache

Scope: Medical Image Analysis is a specialized journal that focuses on the development and application of advanced computational techniques for the analysis of medical images. The scope of the journal encompasses a wide range of topics aimed at improving the interpretation, visualization, and quantification of medical imaging data.
Key areas covered by the journal include:
Image Segmentation: Techniques for partitioning medical images into meaningful regions, such as tissues, organs, or pathologies. This includes algorithms for automatic or semi-automatic segmentation of structures in various imaging modalities (e.g., MRI, CT, ultrasound).
Image Registration: Methods for aligning and co-registering images from different time points, modalities, or patients to facilitate comparison, integration, and analysis. This includes rigid and non-rigid registration techniques.
Feature Extraction and Analysis: Extraction of quantitative features from medical images for the purpose of diagnosis, prognosis, and treatment planning. This includes texture analysis, shape analysis, and functional imaging biomarkers.
Machine Learning and AI in Medical Imaging: Application of machine learning, deep learning, and artificial intelligence techniques to medical image analysis. This includes classification, detection, and prediction models for various clinical tasks.
Image Reconstruction: Development of algorithms for reconstructing high-quality images from raw data acquired by imaging devices. This includes techniques for improving image resolution, reducing noise, and addressing artifacts.
Computer-Aided Diagnosis (CAD): Systems and algorithms designed to assist clinicians in interpreting medical images, including automated detection and diagnosis of diseases such as cancer, cardiovascular conditions, and neurological disorders.
Visualization and Image Rendering: Advanced methods for visualizing medical images in 2D, 3D, and 4D to enhance clinical interpretation and surgical planning. This includes techniques for volume rendering, surface rendering, and virtual reality applications.
Functional and Molecular Imaging: Analysis of images that capture functional and molecular processes within the body, such as PET, SPECT, and functional MRI (fMRI). This includes methods for analyzing dynamic processes and interactions.